Inter-frame prediction-based encoding method and apparatus and inter-frame prediction-based decoding method and apparatus, and chip and storage medium

Abstract

Provided in some embodiments of the present application are an inter-frame prediction-based encoding method and an inter-frame prediction-based decoding method. The inter-frame prediction-based encoding method comprises: determining a prediction mode of a symbol to be encoded, wherein the prediction mode comprises an intra-frame prediction mode and an inter-frame prediction mode; on the basis that the prediction mode is an inter-frame prediction mode, determining an inter-frame context state tree of said symbol; for the inter-frame context state tree, determining a predicted value of said symbol; on the basis of the inter-frame context state tree and the predicted value, determining a context probability model group corresponding to said symbol, wherein different inter-frame context state trees and different predicted values correspond to different context probability model groups; determining a context probability model on the basis of the context probability model group corresponding to said symbol; and encoding said symbol on the basis of the context probability model.

Description

Encoding and decoding methods, devices, chips, and storage media based on inter-frame prediction

[0001] This application claims priority to Chinese patent application No. 202410924225.X, filed on July 10, 2024; Chinese patent application No. 202410923998.6, filed on July 10, 2024; Chinese patent application No. 202410924243.8, filed on July 10, 2024; and Chinese patent application No. 202410923979.3, filed on July 10, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of audio and video technology, and in particular to an encoding and decoding method, apparatus, chip and storage medium based on inter-frame prediction. Background Technology

[0003] Point clouds are three-dimensional data sets composed of a large number of points, each containing spatial location information and possible other attributes (such as color, normal vector, texture coordinates, and intensity values). Point cloud data contains rich spatial and attribute information, therefore it can represent three-dimensional media content and can be configured to provide services such as VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality). However, point cloud data often consists of tens of thousands or even hundreds of thousands of points, resulting in a large data volume. Therefore, an effective method for processing point cloud data and compressing it is needed.

[0004] Summary of the Invention

[0005] To address the issue of large point cloud data volume, this application provides some embodiments to compress point cloud data, thereby reducing the data volume.

[0006] This application provides an inter-frame prediction-based coding method in some embodiments. The method includes: determining a prediction mode for a symbol to be encoded, wherein the prediction mode includes an intra-frame prediction mode and an inter-frame prediction mode; determining an inter-frame context state tree for the symbol to be encoded based on the prediction mode being an inter-frame prediction mode; wherein the inter-frame context state tree for the symbol to be encoded is an independent OBUF inter-frame context state tree; determining a predicted value for the symbol to be encoded based on the inter-frame context state tree and the predicted value; determining a context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; determining a context probability model based on the context probability model group corresponding to the symbol to be encoded; and encoding the symbol to be encoded based on the context probability model.

[0007] Through some embodiments of this application, based on the inter-frame context state tree and the predicted values ​​of the symbols to be encoded, a set of context probability models suitable for each state is selected for the context state tree, thereby making fuller use of the inter-frame prediction information, improving the prediction effect, and reducing the number of bits in the context state tree with almost no impact on coding efficiency, thus saving memory.

[0008] This application provides an encoder based on inter-frame prediction in some embodiments. The encoder includes: a prediction mode determination module configured to determine a prediction mode for a symbol to be encoded, wherein the prediction mode includes an intra-frame prediction mode and an inter-frame prediction mode; a context state tree determination module configured to determine an inter-frame context state tree for the symbol to be encoded based on the prediction mode being an inter-frame prediction mode, wherein the inter-frame context state tree for the symbol to be encoded is an independent OBUF inter-frame context state tree; a prediction module configured to determine a predicted value for the symbol to be encoded based on the inter-frame context state tree; a context probability model group configured to determine a context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value, and to determine a context probability model based on the context probability model group corresponding to the symbol to be encoded, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; and an entropy coding module configured to encode the symbol to be encoded based on the context probability model.

[0009] This application provides a decoding method based on inter-frame prediction in some embodiments. The method includes: determining a prediction mode for a symbol to be decoded, wherein the prediction mode includes an intra-frame prediction mode and an inter-frame prediction mode; determining an inter-frame context state tree for the symbol to be decoded based on the prediction mode being an inter-frame prediction mode; determining a predicted value for the symbol to be decoded based on the inter-frame context state tree; determining a context probability model group corresponding to the symbol to be decoded based on the inter-frame context state tree and the predicted value, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; determining a context probability model based on the context probability model group corresponding to the symbol to be decoded; and decoding the symbol to be decoded based on the context probability model.

[0010] This application provides a decoder based on inter-frame prediction in some embodiments. The decoder includes: a prediction mode determination module for determining a prediction mode of a symbol to be decoded, wherein the prediction mode includes an intra-frame prediction mode and an inter-frame prediction mode; a context state tree determination module configured to determine an inter-frame context state tree of the symbol to be decoded based on the prediction mode being an inter-frame prediction mode; a prediction module configured to determine a predicted value of the symbol to be decoded for the inter-frame context state tree; a context probability model group for determining a context probability model corresponding to the symbol to be decoded based on the inter-frame context state tree and the predicted value, and determining a context probability model based on the context probability model group corresponding to the symbol to be decoded, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; and an entropy decoding module configured to decode the symbol to be decoded based on the context probability model.

[0011] Some embodiments of this application provide an apparatus. The apparatus includes: a memory configured to store computer-readable instructions; and a processor configured to invoke the computer-readable instructions to perform the method described above.

[0012] Some embodiments of this application provide a non-volatile computer storage medium. The non-volatile computer storage medium stores computer-readable instructions that, when invoked by a processor, cause the processor to implement the method described above.

[0013] Some embodiments of this application provide a chip. The chip includes: a memory configured to store computer-readable instructions; and a processor configured to invoke the computer-readable instructions to perform the methods described above. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 shows an exemplary block diagram of a system that can be configured for G-PCC encoding and decoding according to some embodiments;

[0016] Figure 2 shows an exemplary block diagram of a G-PCC encoder provided according to some embodiments;

[0017] Figure 3 shows an exemplary block diagram of a G-PCC decoder 300 provided according to some embodiments;

[0018] Figure 4 illustrates a schematic diagram of Trisoup-based geometric reconstruction according to some embodiments;

[0019] Figure 5 shows an exemplary block diagram of an encoder using OBUF technology according to some embodiments;

[0020] Figure 6 shows an example diagram of neighboring vertices of the edge to be encoded along the x, y, and z axes, according to some embodiments;

[0021] Figure 7 shows an exemplary block diagram of an encoding module provided according to some embodiments;

[0022] Figure 8 shows an example diagram of the information structure of secondary information in an inter-frame context state tree according to some embodiments;

[0023] Figure 9 illustrates an example of the information structure of secondary information in an inter-frame context state tree according to some embodiments;

[0024] Figure 10 illustrates an example of the information structure of secondary information in an inter-frame context state tree according to some embodiments;

[0025] Figure 11 shows an exemplary block diagram of a decoding module provided according to some embodiments;

[0026] Figure 12 illustrates an exemplary flow of the coding process using OBUF technology according to some embodiments;

[0027] Figure 13 shows an exemplary flowchart of the encoding process of Trisoup geometric coding based on inter-frame prediction provided in some embodiments;

[0028] Figure 14 shows an exemplary flowchart of the decoding process of Trisoup geometry decoding based on inter-frame prediction according to some embodiments;

[0029] Figure 15 shows an exemplary block diagram of a hardware structure that can be configured to perform encoding and decoding methods according to some embodiments;

[0030] Figure 16 shows an exemplary flowchart of the encoding process of Trisoup geometric coding based on inter-frame prediction according to some embodiments;

[0031] Figure 17 shows an exemplary flowchart of the decoding process of Trisoup geometry decoding based on inter-frame prediction according to some embodiments. Detailed Implementation

[0032] In some embodiments, the methods provided in some embodiments of this application can encode Trisoup vertices in G-PCC. More specifically, the methods provided in some embodiments of this application can encode position information in Trisoup vertex information in G-PCC.

[0033] In some embodiments, the location information includes N bits. The first bit of the N bits indicates that the location interval of the Trisoup vertex is divided into a left interval and a right interval, and the value of the first bit determines whether the Trisoup vertex is located in the left interval or the right interval. In some embodiments, when the value of the first bit is 0, the value of the first bit indicates that the Trisoup vertex is located in the left interval of the location interval, and when the value of the first bit is 1, the value of the first bit indicates that the Trisoup vertex is located in the right interval of the location interval. In some embodiments, when the value of the first bit is 0, the value of the first bit indicates that the Trisoup vertex is located in the right interval of the location interval, and when the value of the first bit is 1, the value of the first bit indicates that the Trisoup vertex is located in the left interval of the location interval. The other N-1 bits of the N bits, excluding the first bit, indicate that the left or right interval of the Trisoup vertex is divided into m parts, indicating the quantized distance from the Trisoup vertex to the midpoint of the location interval. In some embodiments, dividing the left or right interval of the Trisoup vertex into m parts means that m are equal parts, and the value of m is determined according to the value of N. In some embodiments, m is a power of 2 (N-1). In some embodiments, m may also be less than a power of 2 (N-1). In some embodiments, dividing the left or right interval containing the Trisoup vertex into m equal parts may be an equal division. In some embodiments, further dividing the left or right interval containing the Trisoup vertex into m unequal parts may be an unequal division.

[0034] For example, when N equals 2, the first bit in the position information indicates that the position interval where the Trisoup vertex is located is divided into a left interval and a right interval. When the value of the first bit is 0, it indicates that the Trisoup vertex is in the left interval; when the value of the first bit is 1, the Trisoup vertex is in the right interval. When the Trisoup vertex is in the left interval, the second bit in the position information indicates that the left interval is further divided into two position intervals, one of which is a position interval with a relatively short quantization distance to the midpoint of the current position interval, which can be denoted as the first position interval in the left interval, and the other position interval is a position interval with a relatively long quantization distance to the midpoint of the current position interval, which can be denoted as the second position interval in the left interval. When the Trisoup vertex is in the right interval, the second bit in the position information indicates that the right interval is further divided into two position intervals. One position interval is the one with a relatively short quantization distance to the midpoint of the current position interval, which can be denoted as the first position interval in the right interval. The other position interval is the one with a relatively long quantization distance to the midpoint of the current position interval, which can be denoted as the second position interval in the right interval. For example, when the first bit is 0 and the value of the second bit is 0, it indicates that the Trisoup vertex is located in the left interval, in a position interval with a relatively short quantization distance to the midpoint of the current position interval. When the first bit is 0 and the value of the second bit is 1, it indicates that the Trisoup vertex is located in the left interval, in a position interval with a relatively long quantization distance to the midpoint of the current position interval. Of course, it can be understood that when the first bit is 0 and the value of the second bit is 1, it indicates that the Trisoup vertex is located in a position interval in the left interval that is relatively close to the midpoint of the position interval it is in; when the first bit is 0 and the value of the second bit is 0, it indicates that the Trisoup vertex is located in a position interval in the left interval that is relatively far from the midpoint of the position interval it is in.

[0035] For example, when N equals 3, the first bit in the position information indicates that the position interval where the Trisoup vertex is located is divided into a left interval and a right interval. When the value of the first bit is 0, it indicates that the Trisoup vertex is in the left interval; when the value of the first bit is 1, the Trisoup vertex is in the right interval. When the Trisoup vertex is in the left interval, the second and third bits in the position information indicate that the left interval is further divided into 4 position intervals. The quantization distances of these 4 position intervals to the midpoint of the current position interval are denoted as position interval 1, position interval 2, position interval 3, and position interval 4 in order from closest to farthest. For example, when the value of the second and third bits is 00, it indicates that the Trisoup vertex is located in the left interval with the closest quantization distance to the midpoint of the current position interval, i.e., position interval 1; when the value of the second and third bits is 01, it indicates that the Trisoup vertex is located in the left interval with the closest quantization distance to the midpoint of the current position interval, i.e., position interval 1. When the second and third bits are both 10, the Trisoup vertex is located in a position interval within the left interval that is relatively close to the midpoint of its current position interval, i.e., position interval 2. When the second and third bits are both 10, the Trisoup vertex is located in a position interval within the left interval that is relatively far from the midpoint of its current position interval, i.e., position interval 3. When the second and third bits are both 11, the Trisoup vertex is located in a position interval within the left interval that is farthest from the midpoint of its current position interval, i.e., position interval 4. Alternatively, when the second and third bits are both 11, the Trisoup vertex is located in a position interval within the left interval that is closest to the midpoint of its current position interval, i.e., position interval 1. In other words, this application does not restrict which value corresponds to which position interval.

[0036] For example, when N equals 4, the first bit in the position information indicates that the position interval where the Trisoup vertex is located is divided into a left interval and a right interval. When the value of the first bit is 0, it indicates that the Trisoup vertex is in the left interval; when the value of the first bit is 1, the Trisoup vertex is in the right interval. When the Trisoup vertex is in the left interval, the second, third, and fourth bits in the position information indicate that the left interval is further divided into 8 equal position intervals. The values ​​of the second, third, and fourth bits correspond to which of the 8 position intervals, which is consistent with the concept in the example above, and will not be repeated.

[0037] In some embodiments, the position information includes N bits, each bit indicating that the current position interval of the Trisoup vertex is divided into left and right intervals, and the value of the bit indicates whether the interval in which the Trisoup vertex is located is the left or right interval. The current position interval of the Trisoup vertex is determined based on preceding bits. In some embodiments, when the value of a bit is 0, the value of the bit indicates that the Trisoup vertex is located in the left interval of the current position interval; when the value of a bit is 1, the value of the bit indicates that the Trisoup vertex is located in the right interval of the current position interval. In some embodiments, when the value of a bit is 0, the value of the bit indicates that the Trisoup vertex is located in the right interval of the current position interval; when the value of a bit is 1, the value of the bit indicates that the Trisoup vertex is located in the left interval of the current position interval.

[0038] In some embodiments, the N bits in the location information indicate that the location interval of the Trisoup vertex is divided N times. Specifically, the first bit of the N bits indicates that the location interval of the Trisoup vertex is divided into a left interval and a right interval, and the value of the first bit determines whether the Trisoup vertex is located in the left or right interval. The second bit of the N bits indicates that the interval of the Trisoup vertex is further divided into a left and right interval, and the value of the second bit determines whether the Trisoup vertex is located in the left or right interval of its current interval. Similarly, subsequent bits of the N bits are divided in the same way, and after N divisions, the final location of the Trisoup vertex is determined.

[0039] For example, when N equals 2, the first bit in the position information indicates that the position interval where the Trisoup vertex is located is divided into a first left interval and a first right interval. When the value of the first bit is 0, it indicates that the Trisoup vertex is in the first left interval; when the value of the first bit is 1, the Trisoup vertex is in the first right interval. When the Trisoup vertex is in the first left interval, the second bit in the position information indicates that the first left interval is divided into a second left interval and a second right interval. When the value of the second bit is 0, the Trisoup vertex is in the second left interval; when the value of the second bit is 1, the Trisoup vertex is in the second right interval.

[0040] In some embodiments, the position information includes N bits, which indicate which interval of the edge containing the vertex is currently located within in the Trisoup. For example, when N equals 2 and the edge containing the vertex is divided into 3 intervals, a value of 00 for 2 bits indicates that the vertex is currently located within the first interval of the edge containing the vertex; a value of 01 for 2 bits indicates that the vertex is currently located within the second interval of the edge containing the vertex; and a value of 10 for 2 bits indicates that the vertex is currently located within the third interval of the edge containing the vertex. As another example, when N equals 2 and the edge containing the vertex is divided into 4 intervals, the values ​​of the 2 bits can be 00, 01, 10, and 11, representing the vertex's current location within the first, second, third, and fourth intervals of the edge containing the vertex, respectively.

[0041] In some embodiments, the position information of the vertex to be encoded includes N bits of information. Similarly, the position information of the neighboring compensated reference vertices and neighboring uncompensated reference vertices of the vertex to be encoded also includes N bits of information. The vertex to be encoded can be a Trisoup vertex as mentioned above. The position information of the Trisoup vertex is configured to indicate whether the Trisoup vertex exists on an edge and, if it exists, its position range on the edge. Encoding the position information of the vertex to be encoded requires the use of information from the reference frame. For example, when determining the prediction mode, the prediction effect information of the neighboring compensated reference vertices and neighboring uncompensated reference vertices is needed. When determining the inter-frame context state, the position information of the compensated reference vertices is needed. A compensated reference vertex is a vertex on a reference edge in a compensated reference frame that has the same spatial position as the edge on which the vertex to be encoded is located. An uncompensated reference vertex is a vertex on a reference edge in an uncompensated reference frame that has the same spatial position as the edge on which the vertex to be encoded is located. A neighboring compensated reference vertex is a vertex on a reference edge in a compensated reference frame that has a spatial position adjacent to the edge on which the vertex to be encoded is located. A neighboring uncompensated reference vertex is a vertex on a reference edge in an uncompensated reference frame that has a spatial position adjacent to the edge on which the vertex to be encoded is located. Compensated reference frames are reference frames that have undergone motion compensation, while uncompensated reference frames are reference frames that have not undergone motion compensation.

[0042] In some embodiments, the methods provided in some embodiments of this application can encode the first bit of position information in the Trisoup vertex. In some embodiments, the methods provided in some embodiments of this application include: determining a prediction mode of a symbol to be encoded, wherein the prediction mode includes an intra-frame prediction mode and an inter-frame prediction mode; determining an inter-frame context state tree of the symbol to be encoded based on the prediction mode being an inter-frame prediction mode, wherein the inter-frame context state tree of the symbol to be encoded is an independent OBUF inter-frame context state tree; determining a predicted value of the symbol to be encoded for the inter-frame context state tree; determining a context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; determining a context probability model based on the context probability model group corresponding to the symbol to be encoded; and encoding the symbol to be encoded based on the context probability model. According to the method described in this application, some embodiments of this application select a set of context probability models suitable for each state of the context state tree based on the inter-frame prediction results, thereby making fuller use of the inter-frame prediction information and reducing the number of bits in the context state tree and saving memory without affecting the coding efficiency of the Solid G-PCC set.

[0043] In some embodiments, the vertex to be encoded can be a Trisoup vertex, and the symbol to be encoded can be the first bit or the second bit of the position information in the Trisoup vertex. When the position information of the vertex to be encoded is represented using 3 bits, the symbol to be encoded can also be the third bit of the position information in the Trisoup vertex. The inter-frame context state tree (Intra State) is configured to reflect the context state of the vertex to be encoded, where the context state is the probability that the symbol to be encoded of the neighboring vertices of the vertex to be encoded is 1 or 0. The predicted value is configured to reflect that the value of the symbol to be encoded is 0 or 1. The context probability model is configured to obtain context probabilities. The context probability model group is a collection of context probability models. In some embodiments, the context probability model indicates the probability that the symbol to be encoded is 1; in some embodiments, the context probability model indicates the probability that the symbol to be encoded is 0. In some embodiments, encoding the symbol to be encoded based on the context probability model can be entropy encoding of the symbol to be encoded.

[0044] In some embodiments of this application, the method further includes: judging the inter-frame prediction information as predicted as 0 and predicted as 1 based on a fourth quantity information and a fifth quantity information, thereby updating some inter-frame prediction information from predicted as 0 and predicted as 1 to no prediction. The fourth quantity information is the cumulative sum of the first bits in the encoded vertex position information that are determined to be predicted (including predicted as 0 and predicted as 1), and the fifth quantity information is the cumulative sum of the first bits in the encoded vertex position information. In some embodiments, the above operation can merge some inter-frame context state trees, thereby avoiding slow convergence of the probability corresponding to the inter-frame context state tree and poor prediction effect when inter-frame prediction is used less frequently.

[0045] In some embodiments, the context state tree includes an intra-frame context state tree (Inter State), an inter-frame context state tree one, an inter-frame context state tree two, an inter-frame context state tree three, and an inter-frame context state tree four. In some embodiments, the context state tree may include more context state trees; for example, the number of context state trees can be determined according to N mentioned above, for example, the number of inter-frame context state trees is 2 to the power of N. In some embodiments, the context state tree may be and / or include independent OFUF context state trees. The OFUF context state tree refers to the OFUF tree in an OFUF instance, and each OFUF tree in an OFUF instance is an independent context state tree.

[0046] In some embodiments, the context probability model group may include 13 context probability model groups. Each inter-frame context state tree corresponds to three prediction values: no prediction, prediction 0, and prediction 1. In some embodiments, each context probability model group corresponds to one inter-frame context state tree and one prediction value. For example, in some embodiments, one context probability model group may be configured to determine the context probability model based on an intra-frame context state tree. In some embodiments, the remaining 12 context probability model groups may be configured to determine the context probability model based on inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, inter-frame context state tree four, and their corresponding prediction values. In some embodiments of this application, after determining the context state tree of the first bit in the position information of the vertex to be encoded, the method, during inter-frame prediction, maps the inter-frame context state tree to the corresponding context probability model group according to the prediction value to determine whether the context probability model can be subsequently encoded.

[0047] In some embodiments, the context probability model group includes one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model in the context probability model group. In some embodiments, based on the context state tree, the probability model of the context probability model indicated by the context probability model index can be determined.

[0048] In some embodiments, the symbol to be encoded can be encoded based on the context probability model.

[0049] In some embodiments, the inter-frame prediction-based coding apparatus, storage medium, and method provided in this application are based on the same concept. Since the apparatus, storage medium, and method solve similar technical problems, the implementation of the apparatus and storage medium can refer to each other, and repeated details will not be repeated.

[0050] In some embodiments, the decoding apparatus, storage medium and method based on inter-frame prediction provided in this application are based on the same concept as the encoding apparatus, storage medium and method based on inter-frame prediction provided in this application, and the technical principles they employ are similar. Therefore, the implementation of the decoding apparatus, storage medium and method can refer to the implementation of the encoding apparatus, storage medium and method, and the repeated parts will not be described again.

[0051] In some embodiments, a point cloud refers to a collection of massive three-dimensional points. After acquiring the three-dimensional spatial coordinates of each sampled point on the surface of an object, a collection of points is obtained, called a point cloud. In addition to geometric information, such as geometric coordinates, the points in a point cloud also include other attribute information, such as color, normal vector, reflectivity, transparency, and material type. Point clouds can be obtained in various ways. In some embodiments, acquiring a point cloud includes: using a camera array at a known fixed position in space to observe an object, and using the two-dimensional images captured by the camera array to obtain a three-dimensional representation of the object using relevant algorithms, thereby acquiring the point cloud corresponding to the object. In other embodiments, acquiring a point cloud includes: using a LiDAR scanning device to acquire the point cloud corresponding to the object. The LiDAR scanning device's sensor records the electromagnetic waves emitted by the radar and reflected by the object's surface, thereby obtaining the object's volume information, and acquiring the point cloud corresponding to the object based on the object's volume information. In other embodiments, acquiring a point cloud may further include: using artificial intelligence or computer vision algorithms to create three-dimensional volume information from a two-dimensional image, thereby acquiring the point cloud corresponding to the object.

[0052] Point clouds provide a high-precision 3D representation of the physical world for detailed digitization, and can be widely used in fields such as 3D modeling, smart cities, autonomous navigation systems, and augmented reality. However, due to the massive, unstructured, and unevenly dense nature of the data, point cloud storage and transmission face significant challenges. Therefore, efficient point cloud compression is necessary. Currently, there are two compression standards for point clouds: Geometry-based Point Cloud Compression (G-PCC) and Video-based Point Cloud Compression (V-PCC). The following explains the principles and related algorithms of G-PCC.

[0053] Figure 1 illustrates an exemplary block diagram of a system 100 configured for G-PCC encoding and decoding according to some embodiments of this application. As shown in Figure 1, the system 100 can be configured to encode and / or decode point cloud data, i.e., support point cloud compression and / or decompression. The system can effectively compress and / or decompress point cloud data. In some embodiments, the system 100 can be configured for Solid G-PCC encoding and decoding.

[0054] In some embodiments, the system 100 may include a source device 110. The source device 110 may be configured to provide encoded point cloud data to the target device 120 for decoding.

[0055] In some embodiments, the system 100 may include a target device 120. The target device 120 may be configured to receive and decode encoded point cloud data sent by the source device 110.

[0056] In some embodiments, the system 100 may include a storage device 130. After encoding the point cloud data, the source device 110 can store the encoded point cloud data in the storage device 130. The target device 120 can read the encoded point cloud data from the storage device 130 for decoding.

[0057] In some embodiments, the source device 110 and the target device 120 can be any suitable device, such as one or more of the following: computer, laptop, tablet, set-top box, television, camera, display device, digital media player, video game console, video streaming device, land or sea vehicle, spacecraft, aircraft, robot, LIDAR (Light Detection and Ranging) device, satellite, etc. In some embodiments, the source device 110 and the target device 120 can also be connected wirelessly or via wired communication. In some embodiments, the storage device 130 can be any suitable storage device and / or storage medium, such as a file server, cloud server, communication medium, storage device, etc.

[0058] In some embodiments, the source device 110 may include a data source 111. The data source 111 may be configured to acquire point cloud data, particularly unencoded point cloud data. The data source 111 may be configured to provide a series of consecutive data frames to the G-PCC encoder 113 for encoding. The data source 111 may include a point cloud data acquisition device, which may be any one or more of a camera or sensor, such as a 3D scanner, a LiDAR sensor, and a video camera. In some embodiments, the point cloud data acquisition device may also acquire previously acquired but unencoded historical point cloud data, or acquire corresponding data from a data content provider. In summary, the point cloud data acquisition device is configured to acquire point cloud data that can be configured for encoding, and the method of acquiring point cloud data can be diverse; any method that can obtain point cloud data can serve as the point cloud data acquisition device.

[0059] In some embodiments, the source device 110 may include a first memory 112. The first memory 112 may be configured to store point cloud data from the data source 111. The first memory 112 may be a general-purpose storage device. In some embodiments, the first memory 112 may also be configured to store point cloud data encoded by the G-PCC encoder 113.

[0060] In some embodiments, the source device 110 may include a G-PCC encoder 113. The G-PCC encoder 113 may be configured to encode point cloud data acquired from the data source 111. The G-PCC encoder 113 may rearrange received data frames from their received order into an encoding order that can be configured for encoding. The G-PCC encoder 113 may generate one or more bitstreams corresponding to the point cloud data, wherein the one or more bitstreams include encoded data.

[0061] In some embodiments, the source device 110 may include an output interface 114. The output interface 114 may be configured to output one or more bitstreams generated by the G-PCC encoder 113 to the G-PCC decoder 120 for decoding or to a storage device 130 for storage. In some embodiments, the output interface 114 may be a wireless transmitter / receiver, a modem, a wired network component (e.g., an Ethernet card), a wireless communication component operating according to any of the various IEEE 802.11 standards, or other physical components. The output interface 114 may be configured to transmit encoded data according to cellular communication standards, such as 4G, 4G-LTE, 5G, etc. In some embodiments, the output interface 114 may also be configured to operate according to other wireless standards, such as the IEEE 802.11 specification, the IEEE 802.15 specification (e.g., ZigBee). TM Bluetooth TM Standards, etc., are used to transmit encoded data.

[0062] In some embodiments, the source device 110 may also include any other suitable components that can be configured for point cloud data encoding.

[0063] In some embodiments, the target device 120 may include an application 121. The application 121 may be configured to use decoded point cloud data. For example, the application 121 may determine the position of a specific object using the decoded point cloud data. In some embodiments, the application 121 may include a display configured to display an image generated based on the decoded point cloud data.

[0064] In some embodiments, the target device 120 may include a second memory 122; wherein the second memory 122 may be configured to store decoded point cloud data. In some embodiments, the second memory 122 and the first memory 112 may be memories in different devices. In some embodiments, the second memory 122 and the first memory 112 may also be different memories or the same memories in the same device. In some embodiments, when the second memory 122 and the first memory 112 are in the same device, the first memory 112 and the second memory 122 can be distinguished by storage instructions.

[0065] In some embodiments, the target device 120 may include a G-PCC decoder 123; wherein the G-PCC decoder 123 may be configured to decode encoded point cloud data. The G-PCC decoder 123 may receive one or more bitstreams from the input interface 124 and decode the one or more bitstreams to obtain point cloud data. After obtaining the point cloud data, the G-PCC decoder 123 may store the point cloud data in the second memory 122 or transmit the point cloud data to the application terminal 121.

[0066] In some embodiments, the target device 120 may include an input interface 124; wherein the input interface 124 may receive and / or acquire one or more bitstreams from the source device 110 and / or storage device 130, and transmit the one or more bitstreams to the G-PCC decoder 123 for decoding. In some embodiments, the input interface 124 may be a wireless transmitter / receiver, a modem, a wired network component (e.g., an Ethernet card), a wireless communication component operating according to any of the various IEEE 802.11 standards, or other physical components. The input interface 124 may be configured to transmit encoded data according to cellular communication standards, such as 4G, 4G-LTE, 5G, etc. In some embodiments, the input data 124 may also be configured to transmit data according to other wireless standards, such as the IEEE 802.11 specification, the IEEE 802.15 specification (e.g., ZigBee). TM Bluetooth TM Standards, etc., are used to transmit encoded data.

[0067] In some embodiments, the target device 120 may include any suitable component that can be configured to perform point cloud data decoding.

[0068] In some embodiments, the G-PCC encoder 113 can be any suitable encoding and decoding circuit, and the G-PCC decoder 123 can be any suitable encoding and decoding circuit, such as a microprocessor, digital signal processor, image processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), discrete logic, software, hardware, firmware, or any combination thereof. In some embodiments, when the encoding and / or decoding technology is implemented in software, the source device 110 and the target device 120 can store the software instructions in a suitable non-volatile storage medium and use one or more of the aforementioned processors to invoke the instructions and execute the technology in this application. The G-PCC encoder 113 and the G-PCC decoder 123 can be included in one or more encoders and / or decoders, wherein one or more encoders can be integrated as part of a combined codec (CODEC) in a device, one or more decoders can be integrated as part of a combined codec (CODEC) in a device, and the encoder and decoder can be integrated as part of a combined codec (CODEC) in a device. In some embodiments, a device including the G-PCC encoder 113 and the G-PCC decoder 123 may include at least one or more integrated circuits or microprocessors.

[0069] In some embodiments, the G-PCC encoder 113 and the G-PCC decoder 123 may operate according to an encoding standard, such as the Video Point Cloud Compression (V-PCC) standard or the Geometric Point Cloud Compression (G-PCC) standard. The one or more bitstreams typically include a series of values ​​for syntax elements of knowledge-based encoding decisions (e.g., encoding modes).

[0070] Figure 2 shows an exemplary block diagram of a G-PCC encoder 200 provided according to some embodiments of this application. In some embodiments, the G-PCC encoder 200 may be the G-PCC encoder 113 of Figure 1, or a device including the G-PCC encoder 113 of Figure 1. As shown in Figure 2, the G-PCC encoder 200 may include any suitable unit configured to perform geometric information encoding. In some embodiments, the attribute encoding module 220 may include any suitable unit configured to perform attribute information encoding.

[0071] In some embodiments, the G-PCC encoder 200 can be configured to encode a point cloud to be encoded. The point cloud to be encoded includes geometric information and attribute information.

[0072] In some embodiments, the G-PCC encoder 200 may include the geometry encoding module 210. The geometry encoding module 210 may be configured to encode the geometric information.

[0073] In some embodiments, the geometric encoding module 210 may include a coordinate transformation unit 211; wherein the coordinate transformation unit 211 may be configured to perform coordinate transformation on the geometric information to obtain geometrically transformed geometric information, which ensures that all the point clouds to be encoded are contained within a bounding box. In some embodiments, the bounding box may be the smallest cube capable of completely enclosing all the input point clouds to be encoded, and the origin coordinates of the bounding box may be the minimum values ​​of the point clouds to be encoded in the x, y, and z axes. The geometric information obtained after coordinate transformation is determined based on the position of the point clouds to be encoded relative to the origin coordinates.

[0074] In some embodiments, the geometry encoding module 210 may include the voxelization unit 212, wherein the voxelization unit may be configured to voxelize the coordinate-transformed geometric information. In some embodiments, the voxelization of the coordinate-transformed geometric information by the voxelization unit 212 refers to the voxelization unit 212 quantizing and removing duplicate points from the coordinate-transformed geometric information, thereby scaling the point cloud to be encoded. Since quantization and rounding can result in some points in the point cloud to be encoded having the same position, the voxelization unit 212 needs to determine whether to remove duplicate points based on parameters.

[0075] In some embodiments, the geometry encoding module 210 may include an octree encoding unit 213, which may be configured to perform arithmetic encoding on the voxelized geometry information to obtain the geometry information bitstream of the point cloud to be encoded.

[0076] In some embodiments, the geometry encoding module 210 may include a prediction tree encoding unit 214, which may be configured to perform arithmetic encoding on the voxelized geometry information to obtain the geometry information bitstream of the point cloud to be encoded.

[0077] In some embodiments, the geometry encoding module 210 may include a first arithmetic encoding unit 215. In some embodiments, the G-PCC encoder 200 may perform arithmetic encoding on the voxelized geometric information using the octree encoding unit 213 and the first arithmetic encoding unit 215. In some embodiments, the G-PCC encoder 200 may perform arithmetic encoding on the voxelized geometric information using the prediction tree encoding unit 214 and the first arithmetic encoding unit 215.

[0078] In some embodiments, the octree encoding unit 213 includes an octree partitioning subunit 2131. The octree partitioning subunit 2131 can be configured to perform octree partitioning. In some embodiments, the octree partitioning subunit 2131 can be configured to continuously partition the bounding box into octrees according to a breadth-first search (BFS) traversal, and obtain placeholder codes for the nodes of the octree based on the result of each partition. That is, the octree partitioning subunit 2131 can be configured to sequentially partition the bounding box into sub-cubes, and continue partitioning non-empty sub-cubes (containing points in the point cloud) until the resulting leaf nodes are 1×1×1 unit cubes. In one embodiment, the octree partitioning subunit 2131 can be configured to encode the placeholder codes generated during the octree partitioning process and the number of points contained in the leaf nodes, thereby completing the octree-based geometric encoding and generating a binary code stream. Specifically, based on whether any point is occupied in the eight sub-cubes obtained from each octree partitioning, occupancy information, i.e., 8 bits of binary data, is generated for each of the eight sub-cubes obtained from the current octree partitioning. This generated 8-bit binary code is called the occupancy code. In some embodiments, when a point in the sub-cube is occupied, the corresponding bit value is 1; when no point in the sub-cube is occupied, the corresponding bit value is 0.

[0079] In some embodiments, the octree encoding unit 213 may include a surface fitting subunit 2132 and any suitable unit configured to perform octree-based geometric encoding. In some embodiments, the surface fitting subunit 2132 may be configured to perform surface fitting on the geometric information based on a triangle pool to obtain a surface formed by the distribution of point clouds in each sub-block. When performing surface fitting, the surface fitting subunit 2132 may first perform octree partitioning, but it is not necessary to partition the point cloud to be encoded step by step to a unit cube with a side length of 1×1×1. Instead, partitioning stops when the side length of the sub-block is a preset value. Then, based on the surface formed by the distribution of point clouds in each sub-block, at most twelve vertices generated by the surface and the twelve edges of the sub-block are obtained. For example, the preset value may be 32, 16, or 9. The first arithmetic encoding unit 215 may be configured to sequentially encode the vertex coordinates on the edges of each sub-block to generate a binary geometric information code stream.

[0080] In some embodiments, the prediction tree coding unit 214 includes a prediction tree construction subunit 2141. The prediction tree construction subunit 2141 can be configured to construct a prediction tree structure. In some embodiments, the prediction tree construction subunit 2141 can sort the points in the point cloud to be encoded and construct the prediction tree structure using two different methods (e.g., a high-latency slow method and a low-latency fast method), wherein the sorting can employ unordered, Morton order, azimuth order, and radial distance order, etc. The prediction tree coding unit 214 can include a prediction quantization subunit 2142 and other suitable units configured to perform prediction tree-based geometric coding. The prediction quantization subunit 2142 can traverse each node in the prediction tree structure based on the prediction tree structure, predict several location information of the node by selecting different prediction modes to obtain prediction residuals, and quantize the prediction residuals using quantization parameters to obtain residual quantized values, thereby reducing the amount of data. The prediction modes include at least intra-frame prediction modes and inter-frame prediction modes. The first arithmetic coding unit 215 can perform arithmetic coding on the prediction residuals of the node position information of the prediction tree structure, the prediction tree structure, and quantization parameters through continuous iteration, thereby generating a binary geometric information code stream.

[0081] In some embodiments, the G-PCC encoder 200 may include an attribute encoding module 220. The attribute encoding module 220 may be configured to encode the attribute information.

[0082] In some embodiments, the attribute encoding module 220 may be configured to encode the attribute information using Region Adaptive Hierarchical Transform Coding (RAHT), Interpolation-based Hierarchical Nearest-neighbor Prediction, or Interpolation-based Hierarchical Nearest-neighbor Prediction with an Update / Lifting Step. In some embodiments, the attribute encoding module 220 may include a color conversion unit 221. The color conversion unit 221 may be configured to perform color conversion on the attribute information, converting the RGB components of pixels to YUV components.

[0083] In some embodiments, the attribute encoding module 220 may include a recoloring unit 222. The recoloring unit 222 may be configured to recolor geometric information to obtain attribute values ​​for the reconstructed point cloud. The recoloring refers to assigning attribute information to the reconstructed point cloud using the original point cloud, with the goal of making the attribute values ​​of the reconstructed point cloud as similar as possible to the attribute values ​​of the point cloud to be encoded, thereby minimizing error.

[0084] In some embodiments, the attribute encoding module 220 may include a RAHT (Region Adaptive Hierarchical Transform) unit 223. The RAHT unit 223 may be configured to perform a region adaptive hierarchical transformation on the attribute information to obtain the coefficients of the RAHT transformation.

[0085] In some embodiments, the attribute encoding module 220 may include a quantization unit 224. The quantization unit 224 may be configured to quantize the coefficients of the RAHT transform to obtain quantized RAHT transform coefficients.

[0086] In some embodiments, the attribute encoding module 220 may include a geometry reconstruction unit 226. Since the attribute encoding module 220 processes based on reconstructed geometric information, after lossy geometry encoding is completed, the geometry reconstruction unit 226 may be configured to reconstruct geometric information based on the geometric bitstream, and to match the original attribute information and the reconstructed geometric information.

[0087] In some embodiments, the attribute encoding module 220 may include a second arithmetic encoding module 225. The second arithmetic encoding module 225 performs arithmetic encoding on the coefficients of the quantized RAHT transform to obtain the attribute information bitstream.

[0088] Figure 3 is an exemplary block diagram of a G-PCC decoder 300 provided according to some embodiments of this application. In some embodiments, the G-PCC decoder 300 may be the G-PCC decoder 123 shown in Figure 1. As shown in Figure 3, the G-PCC decoder 300 may include any suitable unit that can be configured to perform geometric information decoding. In some embodiments, the attribute decoding module 320 may include any suitable unit that can be configured to perform attribute information decoding.

[0089] In some embodiments, the G-PCC decoder 300 can be configured to decode the geometric information bitstream and the attribute information bitstream to obtain geometric information and attribute information, and then reconstruct the point cloud.

[0090] In some embodiments, the G-PCC decoder 300 may include a geometry decoding module 310. The geometry decoding module 310 may be configured to decode the geometry information bitstream to obtain geometry information.

[0091] In some embodiments, the geometry decoding module may include a first arithmetic decoding unit 311. The first arithmetic decoding unit 311 may be configured to perform arithmetic decoding on the geometry information bitstream to obtain an arithmetically decoded geometry information bitstream. In some embodiments, the geometry decoding module may include an octree decoding unit 312. The octree decoding unit 312 may be configured to perform octree decoding on the arithmetically decoded geometry information bitstream to obtain placeholder codes.

[0092] In some embodiments, the geometry decoding module may include a prediction tree decoding unit 313. The prediction tree decoding unit 313 may be configured to perform prediction tree decoding on the arithmetic-decoded geometry information bitstream to obtain quantization residuals.

[0093] In some embodiments, the geometry decoding module may include a geometry reconstruction unit 314. The geometry reconstruction unit 314 may be configured to reconstruct geometric information based on the bitstream information to obtain the reconstructed geometric information.

[0094] In some embodiments, the geometry decoding module may include an inverse coordinate transformation unit 315. The inverse coordinate transformation unit 315 may be configured to perform an inverse coordinate transformation on the reconstructed geometric information, thereby outputting the geometric information.

[0095] In some embodiments, the octree decoding unit 312 may include an octree synthesis subunit 3121. The octree synthesis subunit 3121 may be configured to synthesize an octree. In some embodiments, the octree decoding unit 312 may include a surface fitting subunit 3122. The surface fitting subunit 3122 may be configured to perform surface fitting to reconstruct geometric information. In some embodiments, the octree decoding unit 312 may, in a breadth-first traversal order, continuously parse the placeholder code of each node and sequentially divide the nodes until a 1×1×1 unit cube is obtained, stopping the division and parsing the number of points contained in each leaf node, ultimately recovering the geometrically reconstructed point cloud information.

[0096] In some embodiments, the prediction tree decoding unit 313 may include a prediction tree reconstruction subunit 3131. The prediction tree reconstruction subunit 3131 may be configured to reconstruct the prediction tree. In some embodiments, the prediction tree decoding unit 313 may include a residual calculation subunit 3132. The residual calculation subunit 3132 may be configured to obtain the geometric position prediction residual information and quantization parameters of each prediction node and perform inverse quantization, thereby being configured to be used by the geometric reconstruction unit 314 and the inverse coordinate transformation unit 315 to obtain geometric information.

[0097] In some embodiments, the G-PCC decoder 300 may include an attribute decoding module 320. The attribute decoding module 320 may be configured to decode the attribute information bitstream to obtain attribute information.

[0098] In some embodiments, the attribute decoding module 320 may include a second arithmetic decoding unit 331. The second arithmetic decoding unit 331 may be configured to perform arithmetic decoding on the attribute information bitstream.

[0099] In some embodiments, the attribute decoding module 320 may include an inverse quantization unit 332. The inverse quantization unit 332 may be configured to inverse quantize the arithmetic-decoded attribute information bitstream.

[0100] In some embodiments, the attribute decoding module 320 may include a RAHT transformation unit 333. The RAHT transformation unit 333 may be configured to perform an inverse RAHT transformation on the attribute information bitstream.

[0101] In some embodiments, the attribute decoding module 320 may include a color inverse transformation unit 334. The color inverse transformation unit 334 may be configured to perform a color inverse transformation on the inversely transformed attribute information bitstream to obtain attribute information.

[0102] In some embodiments, based on the geometric information obtained by the geometric decoding module 310 and the attribute information obtained by the attribute decoding module 320, the G-PCC decoder can reconstruct the three-dimensional image model of the point cloud data to be encoded.

[0103] In the G-PCC encoder shown in Figure 2 and the G-PCC decoder shown in Figure 3, both the octree encoding unit 213 and the octree decoding unit 312 can perform Trisoup-based geometric encoding and decoding. When performing Trisoup-based geometric encoding, the octree encoding unit 213 can first perform octree partitioning, dividing the point cloud to be encoded into leaf nodes of a specified side length; then, the surface information composed of voxels within the node is represented by a series of triangle meshes, forming a triangular facet. In some embodiments, G-PCC typically uses the parameter trisoup node size to represent the size of the block containing the triangular facet. When the size of the block containing the triangular facet is greater than 0, a geometric facet can represent the set of voxels within the node, and the maximum of twelve intersections between the geometric facet and the twelve edges of the block containing the triangular facet are called vertices.

[0104] In some embodiments, the presence of vertices on the edges of the block represented by each node and the quantized position of the vertices on the edges can be represented as 1 bit and N bits, respectively, to sequentially encode the vertex-related information of the block containing each triangular facet, thereby generating a binary code stream. In some embodiments, N can be greater than or equal to 1, for example, it can be equal to 2. Inside each node, the reconstructed surface is constructed from non-planar polygons formed by the vertices, organized into a set of triangles. In addition, each node has a centroid vertex, and encoding the offset between the final centroid coordinates and the initial centroid coordinates (by means of the surface normal vector n) can improve the representation of the surface curvature within each node. In addition, surface vertices can also be created and labeled. Figure 4 provides a schematic diagram of geometric reconstruction based on Trisoup according to some embodiments of this application. In some embodiments, the octree encoding unit can use fiber optic tracing technology to convert triangular voxels into points, thereby reconstructing the point cloud.

[0105] In some embodiments, as shown in Figure 4, the centroid vertex C is encoded as the drift value of the average centroid of all vertices. The vector represents the normal to the surface of the triangle.

[0106] When performing Trisoup-based geometric decoding, the octree decoding unit 312 can use triangulation technology to decode the geometric coordinates of the point cloud from the triangular facets of the nodes. For example, the octree decoding unit 312 checks whether each voxel in the node cube intersects with the triangular facet. If the voxel in the node cube intersects with the triangular facet, the intersection point is calculated and the decoded cube is output. In some embodiments, the octree decoding unit 312 can use six unit vectors (0, 1, 1), (0, 1, 1), (0, 1, 1), (0, 1, 1), (0, 1, 1), (0, 1, 1) to perform intersection checks, thereby determining whether each unit vector intersects with the triangular facet. In some embodiments, the number of points generated by the G-PCC decoder is determined by the grid distance d.

[0107] As described above, the vertex information includes information indicating whether the vertex exists and the position information of the vertex when it exists. The information indicating whether the vertex exists can be identified by a 1-bit identifier, and the position information can be represented by N bits. Each bit can be configured to divide the current position interval of the vertex into a left interval and a right interval, and N can be an integer greater than or equal to 1.

[0108] In some embodiments, when N equals 2, the position information in the vertex information consists of 2 bits. The first bit is configured to indicate that the position interval of the vertex is divided into a first left interval and a first right interval. When the value of the first bit is 0, the vertex is in the first left interval; when the value of the first bit is 1, the vertex is in the first right interval. When the vertex is in the first left interval, the second bit is configured to indicate that the first left interval is divided into a second left interval and a second right interval. When the value of the second bit is 0, the vertex is in the second left interval; when the value of the second bit is 1, the vertex is in the second right interval.

[0109] In some embodiments, when N equals 2, the position information in the vertex information consists of 2 bits. The first bit is configured to indicate that the position interval of the vertex is located is divided into a left interval and a right interval. When the value of the first bit is 0, the vertex is in the left interval; when the value of the first bit is 1, the vertex is in the right interval. When the vertex is in the left interval, the second bit is configured to indicate that the left interval is divided into a first position interval and a second position interval. When the value of the second bit is 0, the vertex is in the first position interval; when the value of the second bit is 1, the vertex is in the second position interval, wherein the quantization distance from the first position interval to the midpoint of the vertex's current position interval is less than the quantization distance from the second position interval to the midpoint of the vertex's current position interval.

[0110] In some embodiments, when the G-PCC encoder encodes the vertex, it can be considered as encoding the vertex information, that is, encoding the vertex position information, which is encoding the N bits. In some embodiments, when encoding the first bit of the N bits in the vertex position information, the vertex is the vertex to be encoded, and the first bit of the N bits is the symbol to be encoded.

[0111] Figure 5 shows an exemplary block diagram of an encoder 500 using the Optimal Binarization with Update on the Fly (OBUF) technique in some embodiments of this application. In some embodiments, the OBUF technique can quickly and accurately map a series of discrete states of domain information to a fixed number M of adaptive entropy encoders through a look-up table (LUT). This operation allows the information states of the context state tree to no longer correspond one-to-one with the context probability models, but instead updates a fixed number of context probability models with the syntax elements of the current encoded vertex, and updates the mapping relationship after each placeholder code is encoded, thus reducing the number of context probability models.

[0112] As shown in Figure 5, in some embodiments, the encoder 500 may include a context state tree determination module 501; in some embodiments, the encoder 500 may include a mapping module 502; in some embodiments, the encoder 500 may include an encoding module 503 and / or any suitable component that can be configured to encode point cloud data. In some embodiments, the encoder 500 may be and / or include the G-PCC encoder 123 and the geometry encoding unit 210 described in conjunction with Figures 1 and 2.

[0113] In some embodiments, the context state tree determination module 501 can be configured to determine the context state tree of the symbol to be encoded. The context state tree of the symbol to be encoded is composed of the position information of neighboring vertices already encoded in space, and the context state tree can be configured to reflect the context state of the vertex to be encoded. In some embodiments, when the symbol to be encoded is the value of the first bit in the position information of the vertex to be encoded, the context state tree of the symbol to be encoded is the value of the first bit in the position information of the neighboring vertices already encoded. In some embodiments, when the symbol to be encoded is the value of the second bit in the position information of the vertex to be encoded, the context state tree of the symbol to be encoded is the value of the second bit in the position information of the neighboring vertices already encoded. In some embodiments, the vertices of the context state tree for constructing the context state tree of the vertex to be encoded can be found from 12 neighboring vertices. Figure 6 shows an example diagram of neighboring vertices located on the edges to be encoded along the x, y, and z axes. As shown in Figure 6, the neighboring vertices may include one or more of the following categories: (1) vertices on the negative direction of the axis corresponding to the edge to be encoded; (2) vertices on the neighboring edge (directly in contact) that is perpendicular to the edge to be encoded; (3) vertices on the neighboring edge (not directly in contact) that is perpendicular to the edge to be encoded; and (4) vertices on the neighboring edge that is parallel to the edge to be encoded.

[0114] In some embodiments, the context state tree determination module 501 can construct a context state tree for the vertex to be encoded in a certain order (e.g., the order of distances from neighboring vertices to the vertex to be encoded). The position information of the first K vertices in the context state tree is primary information, and the position information of the last L vertices is secondary information. L can be dynamically adjusted during the encoding process; for example, K can be 6 and L can be 17. The vertices in the context state tree are selected from the encoded neighboring vertices of the vertex to be encoded.

[0115] In some embodiments, each context state tree vertex can be either present or absent, i.e., a 1 or 0 state. The context state tree can represent the state information of K+L context state tree vertices, meaning the context state tree can be represented by K+L bits; where the state information of the K+L context state tree vertices can include K primary information and L secondary information. During the encoding process, the encoder can record the number of times each context state tree is used. When the number of times a context state tree is used exceeds a threshold T (e.g., T is 100), a context state tree node is added in the scanning order, i.e., the context state tree is subdivided by introducing a new context state tree node. In subsequent encoding processes, when encoding the vertex to be encoded, the updated context state tree can be used for geometric encoding.

[0116] For example, the symbol to be encoded is the first bit of the vertex to be encoded. The context state tree of the symbol to be encoded is composed of the first bits of the position information of the three neighboring vertices that have already been encoded in the space. The possible cases of the first bit of the position information of the three neighboring vertices include 8: 000, 001, 010, 011, 100, 101, 110, and 111. These 8 possible cases constitute the context state tree of the vertex to be encoded. If the context state tree 011 is used more than the threshold, 011 can be subdivided into 0110 and 0111. At this time, the context state tree of the vertex to be encoded includes 9 cases: 000, 001, 010, 0110, 0111, 100, 101, 110, and 111.

[0117] In some embodiments, the mapping module 502 may be configured to determine the context probability model index i corresponding to the context state tree of the symbol to be encoded based on the mapping relationship. In some embodiments, the mapping module 502 may determine the context probability model index i corresponding to the context state tree based on the context state tree and the mapping relationship lookup table (LUT) before all symbols to be encoded are encoded. The context probability model index i may correspond to the context probability model with index i in one or more context probability model groups in the encoding module 503. For example, the encoding module 503 may include a context probability model group, which may correspond to 32 context probability models (coder). i (i takes the value [0,1,…,31]). In some embodiments, the encoder described above can also be an entropy encoder.

[0118] In some embodiments, the encoding module 503 may be configured to perform geometric encoding and / or entropy encoding on the symbol to be encoded. In some embodiments, the encoding module 503 may include one or more context probability model groups, wherein each of the one or more context probability model groups includes multiple context probability models. The encoding module 503 may perform binary encoding on the symbol to be encoded using the context probability model corresponding to the context state tree of the symbol to be encoded.

[0119] In some embodiments, the encoding module 503 may further include an encoder. The encoder may encode the symbol to be encoded based on the context probability model.

[0120] Figure 7 illustrates an exemplary block diagram of an encoding module 700 that can be configured to perform Trisoup geometric coding based on inter-frame prediction according to some embodiments of this application. In some embodiments, the encoding module 700 may be and / or included in the G-PCC encoder 113, geometric coding unit 210, and encoding module 503 described in conjunction with Figures 1, 2, and 5. In some embodiments, the encoding module 700 may be configured to encode a first bit of the position information in the vertex to be encoded.

[0121] As shown in Figure 7, in some embodiments, the encoding module 700 may include any suitable component that can be configured to perform Trisoup geometric coding based on inter-frame prediction.

[0122] In some embodiments, the encoding module 700 may include a prediction mode determination module 701; wherein the prediction mode determination module 701 may be configured to determine the prediction mode of the symbol to be encoded. In some embodiments, the prediction mode may include intra-frame prediction; in some embodiments, the prediction mode may include inter-frame prediction. Intra-frame prediction utilizes intra-frame information to construct a context state tree of the vertex to be encoded to predict the position information of the vertex to be encoded. Inter-frame prediction utilizes at least inter-frame information to construct context state data of the vertex to be encoded to predict the position information of the vertex to be encoded. In some embodiments, the prediction mode determination module 701 may determine the prediction mode based on a first syntax element and uncompensated reference vertex information, wherein the first syntax element indicates whether inter-frame prediction is enabled, and may be denoted as isInter; the uncompensated reference vertex information indicates whether an uncompensated reference vertex exists, and may be denoted as colocatedVertex. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled or the uncompensated reference vertex information indicates that an uncompensated reference vertex does not exist, the prediction mode determination module 701 may determine that the prediction mode is an intra-frame prediction mode. In some embodiments, isInter = 1 indicates that inter-frame prediction is enabled, and isInter = 0 indicates that inter-frame prediction is disabled. In some embodiments, isInter = 0 indicates that inter-frame prediction is enabled, and isInter = 1 indicates that inter-frame prediction is disabled. In some embodiments, colocatedVertex = 0 indicates that an uncompensated reference vertex exists, and colocatedVertex = 0 indicates that no uncompensated reference vertex exists.

[0123] In some embodiments, when the first syntax element indicates that inter-frame prediction is enabled and the uncompensated reference vertex information indicates the existence of an uncompensated reference vertex, the prediction mode determination module 701 can determine the prediction mode of the symbol to be encoded based on the first quantity information and the second quantity information. The first quantity information, denoted as nBadPredRef2, indicates the number of inaccurate predictions when a neighboring uncompensated reference vertex predicts the second bit of a neighboring vertex, and can be obtained by statistically analyzing the encoded neighboring vertices and uncompensated reference vertices. The second quantity information, denoted as nBadPredComp1, indicates the number of inaccurate predictions when a neighboring compensated reference vertex predicts the first bit of a neighboring vertex, and can be obtained by statistically analyzing the encoded neighboring vertices and compensated reference vertices. In some embodiments, if the first quantity information is less than or equal to a first threshold or the second quantity information is less than or equal to a second threshold, it indicates that the prediction effect of the neighboring compensated reference vertex and neighboring uncompensated reference vertex information of the vertex to be encoded after enabling inter-frame prediction meets a preset condition. If the first quantity information is greater than the first threshold and the second quantity information is greater than the second threshold, it indicates that the prediction effect of the neighboring compensated reference vertex and neighboring uncompensated reference vertex information of the vertex to be encoded after enabling inter-frame prediction does not meet the preset condition. In some embodiments, the preset conditions may include good prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be encoded. For example, the prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be encoded are accurate or very few are inaccurate. For example, in some embodiments, a first quantity information less than or equal to 0 or a second quantity information less than or equal to 4 indicates that the prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be encoded after enabling inter-frame prediction meet the preset conditions; the first quantity information greater than 0 and the second quantity information greater than 4 indicates that the prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be encoded after enabling inter-frame prediction do not meet the preset conditions. In some embodiments, based on the first quantity information being less than or equal to a first threshold or the second quantity information being less than or equal to a second threshold, the prediction mode determination module 701 may be configured to determine the prediction mode as an inter-frame prediction mode; based on the first quantity information being greater than the first threshold and the second quantity information being greater than the second threshold, the prediction mode determination module 701 may be configured to determine the prediction mode as an intra-frame prediction mode.

[0124] In some embodiments, the encoding module 700 may include a context state tree determination module 702; wherein the context state tree determination module 702 may be configured to determine the context state tree of the symbol to be encoded. In some embodiments, when the prediction mode determination module 701 determines that the prediction mode is an intra-frame prediction mode, the context state tree determination module 702 may be configured to determine that the context state tree is an intra-frame context state tree. In some embodiments, when the prediction mode determination module 701 determines that the prediction mode is an inter-frame prediction mode, the context state tree determination module 702 may be configured to determine that the context state tree is an inter-frame context state tree. In some embodiments, the context state tree determination module 702 may determine whether the context state tree is inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, or inter-frame context state tree four based on first indication information (which may be denoted as ctxInter), wherein the first indication information may be determined by the value of TrisoupVerticesPred, representing the position information of the compensation reference vertex.

[0125] In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four. Based on the first bit of the compensation reference vertex, the position interval of the vertex to be encoded is predicted to be either a first left interval or a first right interval. When the vertex to be encoded is in the first left interval, the second bit divides the first left interval into a second left interval and a second right interval. When the vertex to be encoded is in the first right interval, based on the second bit, the first right interval is divided into a third left interval and a third right interval. In some embodiments, when the first indication information is not located in the position interval of the vertex to be encoded, the first indication information is 0; when the first indication information is located in the second left interval, the first indication information is 1; when the first indication information is located in the second right interval, the first indication information is 2; when the first indication information is located in the third left interval, the first indication information is 3; and when the first indication information is located in the third right interval, the first indication information is 4.

[0126] In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four. Based on the first bit of the compensation reference vertex, the position interval of the vertex to be encoded is predicted to be either a left interval or a right interval. When the vertex to be encoded is in a left interval, the second bit divides the left interval into a first position interval and a second position interval. When the vertex to be encoded is in a right interval, based on the second bit, the first right interval is divided into a first position interval and a second position interval. The quantization distance from the first position interval to the midpoint of the current position interval is less than the quantization distance from the second position interval to the midpoint of the current position interval. In some embodiments, when the first indication information is not located in the position interval of the vertex to be encoded, the first indication information is 0; when the first indication information is located in the second position interval of the left interval, the first indication information is -2; when the first indication information is located in the first position interval of the left interval, the first indication information is -1; when the first indication information is located in the first position interval of the right interval, the first indication information is 1; and when the first indication information is located in the second position interval of the right interval, the first indication information is 2. In some embodiments, when the first indication information is 0, the context state tree determination module 702 can be configured to determine that the context state tree is an intra-frame context state tree; when the first indication information is 1, the context state tree determination module 702 can be configured to determine that the context state tree is inter-frame context state tree one in inter-frame context state trees; when the first indication information is 2, the context state tree determination module 702 can be configured to determine that the context state tree is inter-frame context state tree two in inter-frame context state trees; when the first indication information is 3, the context state tree determination module 702 can be configured to determine that the context state tree is inter-frame context state tree three in inter-frame context state trees; when the first indication information is 4, the context state tree determination module 702 can be configured to determine that the context state tree is inter-frame context state tree four in inter-frame context state trees. In this way, classifying the context state trees by the first indication information allows each tree to update its probability independently, without affecting each other, which helps to better predict the probability of the symbols to be encoded and improves coding performance.

[0127] In some embodiments, when the first indication information is 4, the context state tree determination module 702 may be configured to determine that the context state tree is inter-frame context state tree one in the inter-frame context state tree; when the first indication information is 3, the context state tree determination module 702 may be configured to determine that the context state tree is inter-frame context state tree two in the inter-frame context state tree; when the first indication information is 2, the context state tree determination module 702 may be configured to determine that the context state tree is inter-frame context state tree three in the inter-frame context state tree; when the first indication information is 2, the context state tree determination module 702 may be configured to determine that the context state tree is inter-frame context state tree four in the inter-frame context state tree.

[0128] In some embodiments, the minor information in the intra-frame context state tree may include 15 bits of information. The minor information in inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four may each include 17 bits of information. The structures of the intra-frame context state tree and inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four can be referred to Figure 8.

[0129] In some embodiments, the encoding module 700 may include a prediction module 703; wherein the prediction module 703 may be configured to determine the predicted value of the first bit when the prediction mode is an inter-frame prediction mode. In some embodiments, the prediction module 703 may include one or more prediction units 703-1, 703-2, ..., 703-G, wherein G = 4. In some embodiments, G may be determined based on the number of bits N representing the position information in the vertex to be encoded. In some embodiments, each of the one or more prediction units has the same structure.

[0130] In some embodiments, the one or more prediction units may determine the predicted value based on third quantity information and the uncompensated reference vertex information, wherein the third quantity information indicates the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit in the neighboring vertex information, which may be denoted as nBadPredRef2. In some embodiments, the number of bits in the position information of the neighboring uncompensated reference vertex predicting the neighboring vertex information is the same as the number of bits in the position information of the vertex to be encoded. In some embodiments, when the third quantity information indicates that the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit in the neighboring vertex information is greater than a first threshold, this indicates that the prediction effect of the neighboring uncompensated reference vertex predicting the second bit in the neighboring vertex information does not meet a preset condition. When the third quantity information indicates that the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit in the neighboring vertex information is less than or equal to the first threshold, this indicates that the prediction effect of the neighboring uncompensated reference vertex predicting the second bit in the neighboring vertex information meets a preset condition. In some embodiments, the preset condition may include a good prediction effect of the second bit in the neighbor uncompensated reference vertex prediction of the neighbor vertex information. For example, the prediction results of the neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the vertex to be encoded are accurate or very rarely inaccurate. In some embodiments, when the third quantity information indicates that the number of inaccurate predictions when the neighbor uncompensated reference vertex predicts the second bit in the neighbor vertex information is greater than 0, this indicates that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction of the neighbor vertex information does not meet the preset condition. When the third quantity information indicates that the number of inaccurate predictions when the neighbor uncompensated reference vertex predicts the second bit in the neighbor vertex information is less than or equal to 0, this indicates that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction of the neighbor vertex information meets the preset condition.

[0131] In some embodiments, when the prediction effect of the second bit in the neighbor vertex information of the uncompensated neighbor reference vertex does not meet a preset condition or the uncompensated reference vertex information indicates that there is no uncompensated reference vertex, it can be considered that the prediction effect of the neighboring vertices of the vertex to be encoded is not good. In this case, the one or more prediction units can be configured to determine not to use the uncompensated reference vertex information for prediction, i.e., the prediction value is no prediction (NoPred). In some embodiments, when the prediction effect of the second bit in the neighbor vertex information of the uncompensated neighbor reference vertex meets a preset condition and the uncompensated reference vertex information indicates the existence of an uncompensated reference vertex, the one or more prediction units can be configured to determine the prediction value based on the first bit in the position information of the uncompensated reference vertex. In some embodiments, if the first bit in the position information of the uncompensated reference vertex is 0, the one or more prediction units can be configured to determine the prediction value as 0 (Pred 0). If the first bit in the position information of the uncompensated reference vertex is 1, the one or more prediction units can be configured to determine the prediction value as 1 (Pred 1).

[0132] In some embodiments, the encoding module 700 may include a context probability model group 704. In some embodiments, when the prediction mode is an inter-frame prediction mode, the context probability model group 704 may be configured to determine a context probability model for the symbol to be encoded based on the inter-frame context state tree determined by the context state tree determination module 702 and the prediction value determined by the prediction module 703 for subsequent encoding. In some embodiments, when the prediction mode is an intra-frame prediction mode, the context probability model group 704 may be configured to determine a context probability model for the symbol to be encoded based on the intra-frame context state tree for subsequent encoding.

[0133] In some embodiments, the context probability model group 704 may include one or more context probability model groups 704-1, 704-2, ..., 704-H, where, for example, H = 5. In some embodiments, when the prediction mode is an intra-frame prediction mode, the context probability model group 704-1 may be configured to determine a context probability model for subsequent encoding. In some embodiments, when the prediction mode is an inter-frame prediction mode, the context probability model group 704-2 may be configured to determine a context probability model for subsequent encoding based on an inter-frame context state tree one and its corresponding predicted values; the context probability model group 704-3 may be configured to determine a context probability model for subsequent encoding based on an inter-frame context state tree two and its corresponding predicted values; the context probability model group 704-4 may be configured to determine a context probability model for subsequent encoding based on an inter-frame context state tree three and its corresponding predicted values; and the context probability model group 704-5 may be configured to determine a context probability model for subsequent encoding based on an inter-frame context state tree four and its corresponding predicted values. In some embodiments, when N is other values, the context probability model group 704 can set the corresponding inter-frame context state tree and the corresponding context probability model group according to the value of N, and determine that the context probability model is configured to encode the symbol to be encoded based on the corresponding inter-frame context state tree and the corresponding context probability model group. For example, when N is 3, eight inter-frame context state trees and corresponding context probability models can be set.

[0134] In some embodiments, the encoding module 700 may include an entropy encoder 705. The entropy encoder 705 may be configured to entropy encode the symbol to be encoded based on the context probability model.

[0135] In some embodiments, the context probability model group 704 may include one or more context probability model groups 704-1, 704-2, ..., 704-H, where H = 13. In some embodiments, H may be determined based on the number of bits of position information in the vertex to be encoded. In some embodiments, when the prediction mode is intra-frame prediction mode, the context probability model group 704-1 may be configured to determine the context probability model for subsequent encoding. In some embodiments, when the prediction mode is inter-frame prediction mode, each of inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four corresponds to three context probability model groups, for example, inter-frame context state tree one corresponds to three context probability model groups 704-i, 704-i+1, and 704-i+2. Each inter-frame context state tree corresponds to three prediction values, and each prediction value corresponds to one of the three context probability model groups. For example, when the prediction mode is an inter-frame prediction mode and the context state tree is an inter-frame context state tree, the prediction value can be no prediction, prediction 0, and prediction 1. Here, no prediction corresponds to context probability model group 704-i; prediction 0 corresponds to context probability model group 704-i+1; and prediction 1 corresponds to context probability model group 704-i+2. In this way, the prediction probability corresponding to the position information of the vertex to be encoded can be more accurate.

[0136] In some embodiments, when the context state tree is an inter-frame context state tree one, if the predicted value is no prediction, the context probability model group 704 can use context probability model group 704-2; if the predicted value is predicted 0, the context probability model group 704 can use context probability model group 704-3; and if the predicted value is predicted 1, the context probability model group 704 can use context probability model group 704-4. When the context state tree is an inter-frame context state tree two, if the predicted value is no prediction, the context probability model group 704 can use context probability model group 704-5; if the predicted value is predicted 0, the context probability model group 704 can use context probability model group 704-6; and if the predicted value is predicted 1, the context probability model group 704 can use context probability model group 704-7. Similarly, when the context state trees are inter-frame context state tree three and inter-frame context state tree four, the context probability model group used by the context probability model group 704 can be inferred sequentially. In the above scenario, the intra-frame context state tree and the inter-frame context state trees one, two, three, and four can each include 15 bits of information. The secondary information structure of the context state tree can be shown in Figure 9.

[0137] In some embodiments, each context probability model group in the context probability model group 704 includes one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, the probability model of the context probability model i indicated by the context probability model index can be determined.

[0138] The context probability model group 704 uses 13 context probability model groups to select a suitable context probability model group for each context state tree. The suitable context probability model group refers to the context probability model group corresponding to the predicted value of the context state tree. This approach avoids different predicted values ​​of the context state tree sharing the same context probability model group, thereby improving the efficiency of context probability model group probability updates and thus improving prediction performance.

[0139] In some embodiments, after determining the predicted value based on the third quantity information and the uncompensated reference vertex information, one or more prediction units in the prediction module 703 can determine the ratio of a fourth quantity information and a fifth quantity information. The fourth quantity information, denoted as PredNum, indicates the cumulative sum of the first bits in the position information of the encoded vertices that are determined to be predicted (i.e., predicted values ​​of 0 or 1). This fourth quantity information represents the number of encoded vertices determined to be predicted, i.e., the number of encoded vertices with predicted values ​​of 0 and 1. The fifth quantity information, denoted as CoNum, indicates the cumulative sum of the first bits in the position information of the encoded vertices. This fifth quantity information represents the number of encoded vertices with a predicted value of 1. In some embodiments, one or more prediction units in the prediction module 703 can perform a secondary determination of the predicted value by comparing the ratio of the fourth quantity information and the fifth quantity information with a preset threshold.

[0140] In some embodiments, based on the predicted value being 0 or 1, if the ratio of the fourth quantity information to the fifth quantity information is greater than the preset threshold, it can be considered that there are more neighboring vertices selected for prediction, and the reference information is relatively similar to the vertex to be encoded. One or more prediction units in the prediction module 703 can be configured to determine that the predicted value remains unchanged, i.e., based on the ratio of the fourth quantity information to the fifth quantity information being greater than the preset threshold; if the predicted value is 0, one or more prediction units in the prediction module 703 can be configured to determine that the predicted value is 0; if the predicted value is 1, one or more prediction units in the prediction module 703 can be configured to determine that the predicted value is 1. In some embodiments, based on the predicted value being 0 or 1, if the ratio of the fourth quantity information to the fifth quantity information is less than or equal to the preset threshold, it can be considered that the reference information is not sufficiently similar to the vertex to be encoded. One or more prediction units in the prediction module 703 can be configured to determine that the updated predicted value is not predicted, i.e., based on the ratio of the fourth quantity information to the fifth quantity information being less than or equal to the preset threshold; if the predicted value is 0 or 1, one or more prediction units in the prediction module 703 can be configured to determine that the updated predicted value is not predicted. In this way, by comparing the ratio of the fourth and fifth quantitative information with the preset threshold, it can be determined whether to update the predicted value and the updated predicted value when it is necessary, thus obtaining better prediction results.

[0141] In some embodiments, one or more prediction units in the prediction module 703 may be configured such that the determined budget value may include the following cases: 1. No prediction; 2. Predicted value is 0 before update, no prediction after update; 3. Predicted value is 1 before update, no prediction after update; 4. Predicted value is 0; 5. Predicted value is 1.

[0142] The above-described cases correspond to different context probability model groups 704-i within context probability model group 704. In some embodiments, when the context state tree is an inter-frame context state tree one, case 1 corresponds to context probability model group 704-2 within context probability model group 704, i.e., when the predicted value is no prediction, inter-frame context state tree one corresponds to context probability model group 704-2 within context probability model group 704; case 2 corresponds to context probability model group 704-3 within context probability model group 704, i.e., when the predicted value is 0 before the update and no prediction after the update, inter-frame context state tree one corresponds to context probability model group 704-3 within context probability model group 704; case 3 corresponds to context probability model group 704-i within context probability model group 704. Case 4-4, where the predicted value is 1 before the update and no prediction after the update, corresponds to context probability model group 704-4 in context probability model group 704; Case 5 corresponds to context probability model group 704-4 in context probability model group 704, where the predicted value is 0; Case 6, where the predicted value is 1, corresponds to context probability model group 704-4 in context probability model group 704. For the above cases, the secondary information of the intra-frame context state tree can contain 15 bits of information, and the inter-frame context state trees one, two, three, and four can each contain 16 bits of information. The structure of the secondary information of the context state tree can be as shown in Figure 10.

[0143] In summary, although one or more prediction units in the prediction module 703 update some predicted values ​​of 0 and 1 to "no prediction," the index value of the context probability model group in the context probability model group 704 corresponding to the context state tree still uses the index value of the context probability model group corresponding to the previous predicted value. This approach can merge some inter-frame context state trees, avoiding slow convergence of probabilities in the inter-frame context state trees when inter-frame prediction is used less frequently, thus preventing poor prediction performance.

[0144] In an exemplary example, taking the number of bits of position information N=2, the first bit of the position information of the vertex to be encoded as the symbol to be encoded, and the number of inter-frame context state tree model groups H=13 as an example, the encoding process of the symbol to be encoded in this application embodiment is described in detail. Specifically, when inter-frame prediction is enabled (i.e., isInter=1) and there is an uncompensated reference vertex (i.e., colocatedVertex!=0) for the vertex to be encoded, the encoder can obtain the number of inaccurate predictions when the uncompensated reference vertex predicts the second bit of the neighbor vertex, i.e., the first quantity information nBadPredRef2, and the number of inaccurate predictions when the compensated reference vertex predicts the first bit of the neighbor vertex, i.e., the second quantity information nBadPredComp1. If the first quantity information nBadPredRef2 is less than or equal to the first threshold (e.g., 0) or the second quantity information nBadPredComp1 is less than or equal to the second threshold (e.g., 4), the prediction mode is determined to be the inter-frame prediction mode. Next, the value of the first indication information ctxInter is determined based on the position information of the compensated reference vertex. Based on the value of the first indication information, the context state tree of the vertex to be encoded is determined to be either inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, or inter-frame context state tree four. The secondary information of each inter-frame context state tree may include 17 bits of information. Next, the encoder can obtain the number of inaccurate predictions (i.e., the third quantity information nBadPredRef2) when predicting the second bit of the neighbor uncompensated reference vertex's information, and the information colocatedVertex indicating the existence of an uncompensated reference vertex. If the third quantity information nBadPredRef2 is greater than a threshold (e.g., 0) or the uncompensated reference vertex information colocatedVertex indicates the absence of an uncompensated reference vertex, the encoder may not use the uncompensated reference vertex information for prediction, i.e., no prediction (NoPred). In some embodiments, when the prediction effect of the first bit of the neighbor uncompensated reference vertex's information meets a preset condition and the uncompensated reference vertex information indicates the existence of an uncompensated reference vertex, the encoder determines the prediction value based on the first bit of the uncompensated reference vertex's position information. In some embodiments, if the first bit in the position information of the uncompensated reference vertex is 0, the one or more prediction units determine the predicted value to be 0 (i.e., Pred 0). If the first bit in the position information of the uncompensated reference vertex is 1, the one or more prediction units determine the predicted value to be 1 (i.e., Pred 1).

[0145] In other embodiments, the encoder may also predict values ​​of 0 and 1 based on the ratio of a fourth quantity information and a fifth quantity information, wherein the fourth quantity information indicates that the first bit in the encoded vertex position information is determined to be the cumulative sum of predictions, and the fifth quantity information indicates the cumulative sum of the first bit in the encoded vertex position information. In some embodiments, the encoder can determine the predicted values ​​of 0 and 1 by comparing the ratio of the fourth and fifth quantity information with a preset threshold. For example, based on the predicted value of 0 or 1, if the ratio of the fourth and fifth quantity information is greater than the preset threshold, the encoder determines that the predicted value remains unchanged; that is, based on the ratio of the fourth and fifth quantity information being greater than the preset threshold, if the predicted value is 0, the encoder determines the predicted value to be 0; if the predicted value is 1, the encoder determines the predicted value to be 1. In some embodiments, based on the predicted value being 0 or 1, if the ratio of the fourth quantity information to the fifth quantity information is less than or equal to the preset threshold, the encoder can determine that the updated predicted value is not predicted. That is, based on the ratio of the fourth quantity information to the fifth quantity information being less than or equal to the preset threshold, if the predicted value is 0 or 1, the encoder determines that the updated predicted value is not predicted. In the above cases, the predicted value determined by the encoder can include the following situations: 1. No prediction; 2. Predicted value was 0 before the update, and no prediction after the update; 3. Predicted value was 1 before the update, and no prediction after the update; 4. Predicted value was 0; 5. Predicted value was 1.

[0146] In 1307, the encoder can select the context probability model group corresponding to the symbol to be encoded based on the context state tree and the predicted value to determine the context probability model. In some embodiments, the encoder may include one or more context probability model groups 1-H, where, for example, H=5. That is, the encoder may include context probability model groups 1-5. When the prediction mode is intra-frame prediction mode, the context probability model group 1 can be configured to determine the context probability model based on the intra-frame context state tree. When the prediction mode is inter-frame prediction mode, the context probability model group 2 can be configured to determine the context probability model for subsequent encoding based on inter-frame context state tree 1 and its corresponding predicted value; the context probability model group 3 can be configured to determine the context probability model for subsequent encoding based on inter-frame context state tree 2 and its corresponding predicted value; the context probability model group 4 can be configured to determine the context probability model for subsequent encoding based on inter-frame context state tree 3 and its corresponding predicted value; and the context probability model group 5 can be configured to determine the context probability model based on inter-frame context state tree 4 and its corresponding predicted value. In some embodiments, when N is other than a given value, the encoder can set the corresponding inter-frame context state tree and the corresponding context probability model group according to the value of N, and determine the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group. For example, when N is 3, eight inter-frame context state trees and corresponding context probability modules can be set.

[0147] In some embodiments, the encoder may include one or more context probability model groups 1-H, where H = 13. In some embodiments, H may be determined based on the number of bits of position information in the vertex to be encoded. That is, the encoder may include context probability model groups 1-H. When the prediction mode is intra-frame prediction mode, context probability model group 1 may be configured to determine the context probability model based on the intra-frame context state tree. When the prediction mode is inter-frame prediction mode, when the context state tree is inter-frame context state tree 1 and the prediction value is no prediction, context probability model group 2 may be configured to determine the context probability model. When the context state tree is inter-frame context state tree 1 and the prediction value is prediction 0, context probability model group 3 may be configured to determine the context probability model. When the context state tree is inter-frame context state tree 1 and the prediction value is prediction 1, context probability model group 4 may be configured to determine the context probability model. When the context state tree is inter-frame context state tree two, three, or four, the context probability model group 5 to context probability model group 13 can be sequentially deduced according to the case of inter-frame context state tree one and configured to determine the context probability model. In some embodiments, when N is other values, the encoder can set the corresponding inter-frame context state tree and the corresponding context probability model group according to the value of N, and determine the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group.

[0148] In some embodiments, when the encoder determines the predicted value in any of the above five cases, based on the context state tree being inter-frame context state tree one, case 1 corresponds to context probability model group 2, that is, when the predicted value is not predicted, inter-frame context state tree one corresponds to context probability model group 2 in the encoder; case 2 corresponds to context probability model group 3 in the encoder, that is, when the predicted value is 0 before the update and is not predicted after the update, inter-frame context state tree one corresponds to context probability model group 3 in the encoder; case 3 corresponds to context probability model group 4 in the encoder, that is, when the predicted value is 1 before the update and is not predicted after the update, inter-frame context state tree one corresponds to context probability model group 4 in the encoder; case 4 corresponds to context probability model group 3 in the encoder, that is, when the predicted value is 0, inter-frame context state tree one corresponds to context probability model group 3 in the encoder; case 5 corresponds to context probability model group 4 in the encoder, that is, when the predicted value is 1, inter-frame context state tree one corresponds to context probability model group 4 in the encoder. When the context state tree is inter-frame context state tree two, three, or four, the context probability model group 5 to context probability model group 13 can be sequentially deduced according to the case of inter-frame context state tree one and configured to determine the context probability model.

[0149] In some embodiments, each context probability model group includes one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, the probability model of the context probability model i indicated by the context probability model index can be determined.

[0150] In 1308, the encoder encodes the symbol to be encoded based on the context probability model.

[0151] In some embodiments, the encoding method can be configured not only to encode the first bit in the position information of the vertex to be encoded, but also to encode any bit in the position information of the vertex to be encoded.

[0152] In some embodiments, the encoding method described in this application can merge some inter-frame context state trees, avoiding the problem that when inter-frame prediction is used less frequently, the probability convergence of the inter-frame context state tree is slow, resulting in poor prediction performance. Furthermore, the encoding method described in this application can also allow different predicted values ​​to share the same context probability model group, thereby improving the efficiency of probability updates for the context probability model group.

[0153] Figure 14 illustrates an exemplary flowchart of a decoding method 1400 based on inter-frame prediction Trisoup geometric decoding according to some embodiments of this application. In some embodiments, the entity performing the decoding method may be a decoder. The decoder may be and / or a combination of the G-PCC decoder 123 and G-PCC decoder 300 described in Figures 1 and 3.

[0154] As shown in Figure 14, in step 1401, the decoder can determine the prediction mode based on a first syntax element and uncompensated reference vertex information, wherein the first syntax element indicates whether inter-frame prediction is enabled, and the uncompensated reference vertex information indicates whether uncompensated reference vertices exist. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled or the uncompensated reference vertex information indicates that uncompensated reference vertices do not exist, the decoder can determine that the prediction mode is an intra-frame prediction mode, that is, the decoding method flow can proceed to step 1403.

[0155] In step 1402, the decoder can determine the prediction mode based on a first quantity information and a second quantity information. The first quantity information, denoted as nBadPredRef2, indicates the number of inaccurate predictions when a neighboring uncompensated reference vertex predicts the second bit of a neighboring vertex, and can be obtained by statistically analyzing the encoded neighboring vertices and uncompensated reference vertices. The second quantity information, denoted as nBadPredComp1, indicates the number of inaccurate predictions when a neighboring compensated reference vertex predicts the first bit of a neighboring vertex, and can be obtained by statistically analyzing the encoded neighboring vertices and compensated reference vertices. In some embodiments, if the first quantity information is less than or equal to a first threshold, or if the second quantity information is less than or equal to a second threshold, it indicates that the prediction effect of the neighboring compensated reference vertex and neighboring uncompensated reference vertex information of the vertex to be encoded after enabling inter-frame prediction meets a preset condition. If the first quantity information is greater than the first threshold and the second quantity information is greater than the second threshold, it indicates that the prediction effect of the neighboring compensated reference vertex and neighboring uncompensated reference vertex information of the vertex to be encoded after enabling inter-frame prediction does not meet the preset condition. In some embodiments, the preset conditions may include good prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be encoded. For example, the prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be encoded are accurate or very rarely inaccurate. For example, in some embodiments, a first quantity information less than or equal to 0 or a second quantity information less than or equal to 4 indicates that the prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be decoded after enabling inter-frame prediction meet the preset conditions. A first quantity information greater than 0 and a second quantity information greater than 4 indicates that the prediction results for the neighbor compensated reference vertices and neighbor uncompensated reference vertices of the vertex to be decoded after enabling inter-frame prediction do not meet the preset conditions. In some embodiments, based on the first quantity information being less than or equal to a first threshold or the second quantity information being less than or equal to a second threshold, the decoder determines the prediction mode as an inter-frame prediction mode, i.e., the decoding method flow can proceed to 1404; based on the first quantity information being greater than the first threshold and the second quantity information being greater than the second threshold, the prediction mode determination module 701 can be configured to determine the prediction mode as an intra-frame prediction mode, i.e., the decoding method flow can proceed to 1403.

[0156] In step 1403, the decoder determines that the prediction mode is an intra-frame prediction mode.

[0157] In 1404, the decoder determines that the prediction mode is an inter-frame prediction mode.

[0158] In step 1405, the decoder can determine the context state tree based on first indication information, wherein the first indication information indicates the position information of the compensation reference vertex. In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four. Based on the first bit of the compensation reference vertex, the decoder predicts whether the position interval of the vertex to be decoded is a first left interval or a first right interval. When the vertex to be decoded is in the first left interval, the second bit divides the first left interval into a second left interval and a second right interval. When the vertex is in the first right interval, the second bit divides the first right interval into a third left interval and a third right interval. In some embodiments, when the first indication information indicates that the vertex to be decoded is not located in the position interval, the first indication information is 0; when the first indication information indicates that the vertex is located in the second left interval, the first indication information is 1; when the first indication information indicates that the vertex is located in the second right interval, the first indication information is 2; when the first indication information indicates that the vertex is located in the third left interval, the first indication information is 3; and when the first indication information indicates that the vertex is located in the third right interval, the first indication information is 4.

[0159] In some embodiments, when the first indication information is 0, the decoder determines that the context state tree is an intra-frame context state tree; when the first indication information is 1, the decoder determines that the context state tree is inter-frame context state tree one in the inter-frame context state tree; when the first indication information is 2, the decoder determines that the context state tree is inter-frame context state tree two in the inter-frame context state tree; when the first indication information is 3, the decoder determines that the context state tree is inter-frame context state tree three in the inter-frame context state tree; when the first indication information is 4, the decoder determines that the context state tree is inter-frame context state tree four in the inter-frame context state tree.

[0160] In some embodiments, at 1405, the decoder can determine the context state tree based on first indication information, wherein the first indication information indicates the position information of the compensation reference vertex. In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, and inter-frame context state tree four. Furthermore, based on the first bit of the compensation reference vertex, the position interval of the vertex to be decoded is predicted to be either a left interval or a right interval. When the vertex to be decoded is in a left interval, the second bit divides the left interval into a first position interval and a second position interval; when the vertex is in a right interval, the second bit divides the right interval into a first position interval and a second position interval. The quantization distance from the first position interval to the midpoint of the current position interval is less than the quantization distance from the second position interval to the midpoint of the current position interval. In some embodiments, when the first indication information indicates that it is not located in the position interval of the vertex to be decoded, the first indication information is 0; when the first indication information indicates that it is located in the second position interval in the left interval, the first indication information is -2; when the first indication information indicates that it is located in the first position interval in the left interval, the first indication information is -1; when the first indication information indicates that it is located in the first position interval in the right interval, the first indication information is 1; and when the first indication information indicates that it is located in the second position interval in the right interval, the first indication information is 2.

[0161] In some embodiments, when the first indication information is 0, the decoder determines that the context state tree is an intra-frame context state tree; when the first indication information is -2, the decoder determines that the context state tree is inter-frame context state tree one in the inter-frame context state tree; when the first indication information is -1, the decoder determines that the context state tree is inter-frame context state tree two in the inter-frame context state tree; when the first indication information is 1, the decoder determines that the context state tree is inter-frame context state tree three in the inter-frame context state tree; and when the first indication information is 2, the decoder determines that the context state tree is inter-frame context state tree four in the inter-frame context state tree.

[0162] In step 1406, the decoder can determine the predicted value of the symbol to be decoded. In some embodiments, the decoder can determine the predicted value based on a third quantity information and the uncompensated reference vertex information, wherein the third quantity information indicates the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit in the neighboring vertex information, which can be denoted as nBadPredRef2. In some embodiments, when the third quantity information indicates that the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit in the neighboring vertex information is greater than a first threshold, this indicates that the prediction effect of the neighboring uncompensated reference vertex predicting the second bit in the neighboring vertex information does not meet a preset condition. When the third quantity information indicates that the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit in the neighboring vertex information is less than or equal to the first threshold, this indicates that the prediction effect of the neighboring uncompensated reference vertex predicting the second bit in the neighboring vertex information meets a preset condition. In some embodiments, the preset condition may include that the prediction effect of the neighboring uncompensated reference vertex predicting the second bit in the neighboring vertex information is good, for example, the prediction results of the neighboring compensated vertices and neighboring uncompensated vertex information of the vertex to be encoded are accurate or very few are inaccurate. In some embodiments, when the third quantity information indicates that the number of inaccurate predictions when predicting the second bit of the neighbor uncompensated reference vertex in the neighbor vertex information is greater than 0, this indicates that the prediction effect of the neighbor uncompensated reference vertex in predicting the second bit of the neighbor vertex information does not meet a preset condition. When the third quantity information indicates that the number of inaccurate predictions when predicting the second bit of the neighbor uncompensated reference vertex in the neighbor vertex information is less than or equal to 0, this indicates that the prediction effect of the neighbor uncompensated reference vertex in predicting the second bit of the neighbor vertex information meets a preset condition. In some embodiments, when the prediction effect of the neighbor uncompensated reference vertex in predicting the second bit of the neighbor vertex information does not meet a preset condition or the uncompensated reference vertex information indicates that there is no uncompensated reference vertex, the decoder determines not to use the uncompensated reference vertex information for prediction, that is, the prediction value is no prediction (i.e., NoPred). In some embodiments, when the prediction effect of the neighbor uncompensated reference vertex in predicting the second bit of the neighbor vertex information meets a preset condition and the uncompensated reference vertex information indicates that there is an uncompensated reference vertex, the decoder determines the prediction value based on the first bit in the position information of the uncompensated reference vertex. If the first bit in the position information of the uncompensated reference vertex is 0, then the one or more prediction units determine the predicted value to be 0 (i.e., Pred 0). If the first bit in the position information of the uncompensated reference vertex is 1, then the one or more prediction units determine the predicted value to be 1 (i.e., Pred 1).

[0163] In some embodiments, the decoder can also determine the predicted values ​​of 0 and 1 based on the ratio of a fourth quantity information and a fifth quantity information. The fourth quantity information, denoted as PredNum, indicates the cumulative sum of the first bits in the position information of the decoded vertices that are determined to be predicted, representing the number of encoded vertices determined to be predicted, i.e., the number of encoded vertices with predicted values ​​of 0 and 1. The fifth quantity information, denoted as CoNum, indicates the cumulative sum of the first bits in the position information of the decoded vertices, representing the number of encoded vertices with a predicted value of 1. In some embodiments, the decoder can determine the predicted values ​​of 0 and 1 by comparing the ratio of the fourth and fifth quantity information with a preset threshold. For example, based on whether the predicted value is 0 or 1, if the ratio of the fourth and fifth quantity information is greater than the preset threshold, the decoder determines that the predicted value remains unchanged; that is, based on the ratio of the fourth and fifth quantity information being greater than the preset threshold, if the predicted value is 0, the decoder determines the predicted value to be 0; if the predicted value is 1, the decoder determines the predicted value to be 1. In some embodiments, based on the predicted value being 0 or 1, if the ratio of the fourth quantity information to the fifth quantity information is less than or equal to the preset threshold, the decoder determines that the updated predicted value is not predicted. That is, based on the ratio of the fourth quantity information to the fifth quantity information being less than or equal to the preset threshold, if the predicted value is 0 or 1, the decoder determines that the updated predicted value is not predicted. In the above cases, the predicted value determined by the decoder may include the following situations: 1. No prediction; 2. Predicted value was 0 before the update, and no prediction after the update; 3. Predicted value was 1 before the update, and no prediction after the update; 4. Predicted value was 0; 5. Predicted value was 1.

[0164] In step 1407, the decoder can determine a context probability model for the symbol to be decoded based on the context state tree and the predicted value for subsequent decoding. In some embodiments, the decoder may include one or more context probability model groups 1-H, where H=5. That is, the decoder may include context probability model groups 1-5. When the prediction mode is intra-frame prediction mode, context probability model group 1 can be configured to determine the context probability model based on the intra-frame context state tree for subsequent decoding. When the prediction mode is inter-frame prediction mode, context probability model groups 2-5 are respectively configured to determine the context probability model based on inter-frame context state tree 1, inter-frame context state tree 2, inter-frame context state tree 3, inter-frame context state tree 4 and their corresponding predicted values ​​for subsequent decoding. In some embodiments, when N is other values, the decoder can set the corresponding inter-frame context state tree and the corresponding context probability model group according to the value of N, and determine the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group for subsequent decoding.

[0165] In some embodiments, the decoder may include one or more context probability model groups 1-H, where H = 13. In some embodiments, H may be determined based on the number of bits of position information in the vertex to be decoded. The decoder may include context probability model groups 1-3. When the prediction mode is intra-frame prediction mode, context probability model group 1 may be configured to determine the context probability model based on the intra-frame context state tree for subsequent decoding. When the prediction mode is inter-frame prediction mode, if the context state tree is inter-frame context state tree 1 and the prediction value is not predicted, context probability model group 2 may be configured to determine the context probability model for subsequent decoding. If the context state tree is inter-frame context state tree 1 and the prediction value is predicted 0, context probability model group 3 may be configured to determine the context probability model for subsequent decoding. If the context state tree is inter-frame context state tree 1 and the prediction value is predicted 1, context probability model group 4 may be configured to determine the context probability model is used for subsequent decoding. When the context state tree is inter-frame context state tree two, three, or four, the context probability model group 5 to context probability model group 13 can be sequentially inferred and configured for binary decoding according to the case of inter-frame context state tree one. In some embodiments, when N is other values, the decoder can set the corresponding inter-frame context state tree and the corresponding context probability model group according to the value of N, and determine the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group for subsequent decoding.

[0166] In some embodiments, when the predicted value determined by the decoder falls into one of the above five categories, and the context state tree is an inter-frame context state tree one, the above category 1 corresponds to context probability model group 2, that is, when the predicted value is not predicted, the inter-frame context state tree one corresponds to context probability model group 2 in the decoder; the above category 2 corresponds to context probability model group 3 in the decoder, that is, when the predicted value is 0 before the update and is not predicted after the update, the inter-frame context state tree one corresponds to context probability model group 3 in the decoder; the above category 3 corresponds to context probability model group 4 in the decoder, that is, when the predicted value is 1 before the update and is not predicted after the update, the inter-frame context state tree one corresponds to context probability model group 4 in the decoder; the above category 4 corresponds to context probability model group 3 in the decoder, that is, when the predicted value is 0, the inter-frame context state tree one corresponds to context probability model group 3 in the decoder; the above category 5 corresponds to context probability model group 4 in the decoder, that is, when the predicted value is 1, the inter-frame context state tree one corresponds to context probability model group 4 in the decoder. When the context state tree is inter-frame context state tree two, three, or four, the context probability model group 5 to context probability model group 13 can be sequentially deduced and configured for binary decoding according to the case of inter-frame context state tree one.

[0167] In some embodiments, each context probability model group includes one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, the probability model of the context probability model i indicated by the decoder index can be determined.

[0168] In step 1408, the decoder decodes the symbol to be decoded based on the context probability model.

[0169] In some embodiments, the decoding method can be configured not only to decode the first bit in the position information of the vertex to be decoded, but also to decode any bit in the position information of the vertex to be decoded.

[0170] In an exemplary example, taking the number of bits of location information N=2, the first bit of the location information of the vertex to be decoded as the symbol to be decoded, and the number of inter-frame context state tree model groups H=13 as an example, the decoding process of the symbol to be decoded in this application embodiment is described in detail. Specifically, when inter-frame prediction is enabled (i.e., isInter=1) and there is an uncompensated reference vertex (i.e., colocatedVertex!=0) for the vertex to be decoded, the decoder can obtain the number of inaccurate predictions when the uncompensated reference vertex predicts the second bit of the neighbor vertex, i.e., the first quantity information nBadPredRef2, and the number of inaccurate predictions when the compensated reference vertex predicts the first bit of the neighbor vertex, i.e., the second quantity information nBadPredComp1. If the first quantity information nBadPredRef2 is less than or equal to the first threshold (e.g., 0) or the second quantity information nBadPredComp1 is less than or equal to the second threshold (e.g., 4), the prediction mode is determined to be the inter-frame prediction mode. Next, the value of the first indication information ctxInter is determined based on the position information of the compensation reference vertex. Based on the value of the first indication information, the context state tree of the vertex to be decoded is determined to be inter-frame context state tree one, inter-frame context state tree two, inter-frame context state tree three, or inter-frame context state tree four. The secondary information of each inter-frame context state tree can include 17 bits of information. Next, the decoder can obtain the number of inaccurate predictions (i.e., the third quantity information nBadPredRef2) in the second bit of the neighbor uncompensated reference vertex prediction information, along with the information colocatedVertex indicating the existence of an uncompensated reference vertex. If the third quantity information nBadPredRef2 is greater than a threshold (e.g., 0) or the uncompensated reference vertex information colocatedVertex indicates the absence of an uncompensated reference vertex, the decoder may not use the uncompensated reference vertex information for prediction, i.e., no prediction (NoPred). If the third quantity information nBadPredRef2 is less than or equal to the threshold (e.g., 0) and the uncompensated reference vertex information colocatedVertex indicates the existence of an uncompensated reference vertex, the decoder can determine the prediction value based on the first bit of the uncompensated reference vertex's position information. If the first bit is 0, the prediction value is 0 (i.e., Pred 0); if the first bit is 1, the prediction value is 1 (i.e., Pred 1). Finally, based on the determined inter-frame context state tree and the prediction value, the decoder determines the corresponding context probability model group, determines the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group, and decodes the symbol to be decoded based on the context probability model.

[0171] Figure 15 shows an exemplary block diagram of a hardware structure 1500 that can be configured to perform encoding and decoding methods according to some embodiments of this application. As shown in Figure 15, the hardware structure 1500 may include any suitable components that can be configured to perform the encoding and decoding methods.

[0172] In some embodiments, the processor 1501 may be configured to execute the encoding and / or decoding methods provided in the embodiments of this application. The memory 1502 may be configured to store instructions, programs, and / or code implementing the encoding and / or decoding methods provided in the embodiments of this application. The communication interface 1503 may be configured for the hardware structure 1500 to communicate with other devices or may be configured to communicate with internal modules.

[0173] In some embodiments, the hardware structure 1500 may include a processor 1501. The processor 1501 may include one or more of the following: a microcontroller, a reduced instruction set computer, an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microprocessor unit, a digital signal processor (DSP), and a field-programmable gate array (FPGA), or other circuits or processors capable of executing computer-readable instructions.

[0174] In some embodiments, the hardware structure 1500 may include a memory 1502. The memory 1502 may be a device that stores information using electrical energy, such as various types of memory, random access memory (RAM), and read-only memory (ROM). The random access memory may include one or a combination of several of the following: decimal counters, selectors, delay line memory, Williams transistors, dynamic random access memory (DRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero-capacitance random access memory (Z-RAM). Read-only memory (ROM) can include one or more of the following: bubble memory, magnetic dot memory, thin-film memory, magnetically plated wire memory, magnetic core memory, magnetic drum memory, optical disc drive, hard disk, magnetic tape, early non-volatile memory (NVRAM), phase change memory, magnetoresistive random access memory, ferroelectric random access memory, non-volatile SRAM, flash memory, electronically erasable rewritable ROM, erasable programmable ROM, programmable ROM, shielded stacked read-only memory, floating-gate random access memory, nanometer random access memory, raceway memory, variable resistivity memory, and programmable metallized cells. In some embodiments, memory 1502 can be a device that stores information using magnetic energy, such as hard disk, floppy disk, magnetic tape, magnetic core memory, bubble memory, USB flash drive, and flash memory. In some embodiments, memory 1502 can be a device that stores information using optical methods, such as CD or DVD. In some embodiments, memory 1502 can be a device that stores information using magneto-optical methods, such as magneto-optical disk. In some embodiments, the access method of memory 1502 can be one or more combinations of random access memory, serial access memory, and read-only memory. In some embodiments, memory 1502 may be a non-permanent memory or a permanent memory. In some embodiments, memory 1502 may be local or remote (e.g., memory 1502 may be on a cloud server).

[0175] In some embodiments, the hardware interface may include a communication interface 1503. The communication interface 1503 may be a wired network interface or a wireless network interface. The communication interface 1503 may include one or more combinations of metal cables, optical fibers, hybrid cables and connecting circuits, or other wired network interfaces. The communication interface 1503 may include one or more combinations of wireless local area network (WLAN) interfaces, local area network (LAN) interfaces, wide area network (WAN) interfaces, Bluetooth interfaces, ZigBee interfaces, and near field communication (NFC) interfaces. In some embodiments, the communication interface 1503 may also be a wireless transmitter / receiver, a modem, a wired network component (e.g., an Ethernet card), a wireless communication component operating according to any of the various IEEE 802.11 standards, or other physical components. The communication interface 1503 may be configured to transmit encoded or decoded data according to cellular communication standards, such as 4G, 4G-LTE, 5G, etc. In some embodiments, the communication interface 1503 may also be configured to conform to other wireless standards, such as the IEEE 802.11 specification, the IEEE 802.15 specification (e.g., ZigBee). TM Bluetooth TM Standards, etc., are used to transmit encoded or decoded data.

[0176] In some embodiments of this application, the methods provided further determine whether the inter-frame prediction information is predicted as 0 or 1 based on a sixth quantity of information and a seventh quantity of information, thereby updating some of the inter-frame prediction information from prediction 0 and prediction 1 to no prediction. The sixth quantity of information is the cumulative sum of the second bits of the encoded vertex position information that are judged to be located and predicted, and the seventh quantity of information is the cumulative sum of the second bits of the encoded vertex position information. In some embodiments, the above operation can merge some inter-frame context state trees, thereby avoiding slow convergence of probabilities and poor prediction performance when inter-frame prediction is used less frequently.

[0177] In some embodiments, the context state tree may include an intra-frame context state tree, an inter-frame context state tree one, and an inter-frame context state tree two. In some embodiments, the context state tree may include more context state trees; for example, the number of context state trees can be determined based on N as described above.

[0178] In some embodiments, the context probability model group may include seven context probability model groups. Each inter-frame context state tree corresponds to three prediction values: no prediction, prediction 0, and prediction 1. In some embodiments, each context probability model group corresponds to one context state tree and one prediction value. For example, in some embodiments, one context probability model group may be configured to determine the context probability model based on an intra-frame context state tree. In some embodiments, the remaining six context probability model groups may be configured to determine the context probability model based on inter-frame context state tree one and inter-frame context state tree two and their corresponding prediction values. In some embodiments of this application, after determining the context state tree of the second bit of the vertex position information, the method maps the inter-frame context state tree to the corresponding context probability model group according to the prediction value during inter-frame prediction to determine the context probability model that can be subsequently encoded.

[0179] In some embodiments, when the G-PCC encoder encodes the vertex, it can be considered as encoding the vertex information, that is, encoding the vertex's position information, which is equivalent to encoding the N bits. In some embodiments, when encoding the second bit of the N bits in the vertex's position information, the vertex is the vertex to be encoded, and the second bit of the N bits is the symbol to be encoded.

[0180] Figure 7 illustrates an exemplary block diagram of an encoding module 700 that can be configured to perform Trisoup geometric coding based on inter-frame prediction according to some embodiments of the present disclosure. In some embodiments, the encoding module 700 can be configured to encode a second bit of the position information in the vertex to be encoded.

[0181] In some embodiments, the encoding module 700 may include a prediction mode determination module 701. The prediction mode determination module 701 may be configured to determine the prediction mode of the symbol to be encoded. In some embodiments, the prediction mode may include intra-frame prediction. In some embodiments, the prediction mode may include inter-frame prediction. In some embodiments, the prediction mode determination module 701 may determine the prediction mode based on a first syntax element and compensation reference vertex information, wherein the first syntax element may be configured to indicate whether inter-frame prediction is enabled, and the compensation reference vertex information may be configured to indicate whether a compensation reference vertex for the vertex to be encoded exists after enabling the inter-frame prediction mode. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled or the compensation reference vertex information may be configured to indicate that a compensation reference vertex for the vertex to be encoded does not exist after enabling the inter-frame prediction mode, the prediction mode determination module 701 may determine that the prediction mode is an intra-frame prediction mode.

[0182] In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one and inter-frame context state tree two. Based on the first bit of the compensation reference vertex, the predicted position interval of the vertex to be encoded is divided into a first left interval or a first right interval. When the vertex to be encoded is in the first left interval, the second bit divides the first left interval into a second left interval and a second right interval. When the vertex to be encoded is in the first right interval, the second bit divides the first right interval into a third left interval and a third right interval.

[0183] In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one and inter-frame context state tree two. Based on the first bit of the compensation reference vertex, the predicted position interval of the vertex to be encoded is divided into a left interval or a right interval. When the vertex to be encoded is in the left interval, the second bit divides the left interval into a first position interval and a second position interval. When the vertex to be encoded is in the right interval, the second bit divides the right interval into a first position interval and a second position interval. The quantization distance from the first position interval to the midpoint of the current position interval is less than the quantization distance from the second position interval to the midpoint of the current position interval.

[0184] In some embodiments, when the co-occurrence bit information of the compensation reference vertex information is 0, the context state tree determination module 702 can determine that the inter-frame context state tree is inter-frame context state tree one. When the co-occurrence bit information of the compensation reference vertex information is 1, the context state tree determination module 702 can determine that the inter-frame context state tree is inter-frame context state tree two.

[0185] In some embodiments, the minor information in the intra-frame context state tree may include 15 bits of information. The minor information in the inter-frame context state tree one and inter-frame context state tree two may include 17 bits of information. The structure of the intra-frame context state tree and the inter-frame context state tree one and inter-frame context state tree two can be referred to Figure 8.

[0186] In some embodiments, the encoding module 700 may include a prediction module 703. The prediction module 703 may be configured to determine the predicted value of the symbol to be encoded when the prediction mode is inter-frame prediction mode. In some embodiments, the prediction module 703 includes one or more prediction units 703-1, 703-2, ..., 703-G, where G = 2. In some embodiments, G may also be determined based on the number of bits N of position information in the vertex to be encoded. In some embodiments, each of the one or more prediction units has the same structure.

[0187] In some embodiments, the one or more prediction units determine the predicted value based on the second syntax element and the co-occurrence bit information of the uncompensated reference vertex information of the vertex to be encoded, wherein the value of the second syntax element is determined based on the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex, the uncompensated reference vertex information, and the prediction result of the first bit of the uncompensated reference vertex. In some embodiments, if the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex does not meet a preset condition, or if the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist, or if the prediction result of the first bit of the uncompensated reference vertex does not meet a preset condition, the value of the second syntax element is not equal to 1; if the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex meets a preset condition, or if the uncompensated reference vertex information indicates that the uncompensated reference vertex exists and the prediction result of the first bit of the uncompensated reference vertex meets a preset condition, the value of the second syntax element is equal to 1. In some embodiments, the preset condition means that the prediction result of the second bit of the neighboring vertex predicting the neighboring vertex or the prediction result of the first bit of the uncompensated reference vertex is good, that is, the prediction accuracy of the second bit of the neighboring vertex predicting the neighboring vertex or the first bit of the uncompensated reference vertex is high.

[0188] In some embodiments, based on the fact that the value of the second syntax element is not equal to 1, the one or more prediction units can determine that the symbol to be encoded is not predicted using uncompensated reference vertex information, i.e., the predicted value is no prediction (NoPred). In some embodiments, based on the fact that the value of the second syntax element is equal to 1, the one or more prediction units can determine the predicted value of the symbol to be encoded based on the parity bit information of the uncompensated reference vertex information of the vertex to be encoded. Based on the factity bit information of the uncompensated reference vertex information of the vertex to be encoded being 0, the one or more prediction units can determine that the predicted value of the symbol to be encoded is 0 (i.e., Pred 0). Based on the factity bit information of the uncompensated reference vertex information of the vertex to be encoded being 1, the one or more prediction units can determine that the predicted value of the symbol to be encoded is 1 (Pred 1).

[0189] In some embodiments, based on the predicted value of the symbol to be encoded being 0 or 1, the one or more prediction units may update the predicted value of the symbol to be encoded based on a sixth quantity information and a seventh quantity information, wherein the sixth quantity information is the cumulative sum of the predicted second bits of the encoded vertex position information, and the seventh quantity information is the cumulative sum of the second bits of the encoded vertex position information. In some embodiments, the one or more prediction units may update the predicted value of the symbol to be encoded based on whether the ratio of the sixth quantity information and the seventh quantity information is greater than a preset threshold. In some embodiments, based on the ratio of the sixth quantity information and the seventh quantity information not being greater than the preset threshold, the one or more prediction units may update the predicted value of the symbol to be encoded to not predict; that is, when the predicted value is 0 or 1, based on the ratio of the sixth quantity information and the seventh quantity information not being greater than the preset threshold, the predicted value is updated to not predict. In some embodiments, the predicted value is not updated if the ratio of the sixth quantity information to the seventh quantity information is greater than the preset threshold; that is, when the predicted value is 0 or 1, the predicted value remains the original predicted value of 0 or 1 if the ratio of the sixth quantity information to the seventh quantity information is greater than the preset threshold.

[0190] In some embodiments, the budget value determined by one or more prediction units in the prediction module 703 may include the following cases: 1. No prediction; 2. Prediction value is 0 before update, no prediction after update; 3. Prediction value is 1 before update, no prediction after update; 4. Prediction value is 0; 5. Prediction value is 1.

[0191] In some embodiments, when the prediction mode is an inter-frame prediction mode, the context probability model group 704 is configured to encode the symbol to be encoded based on the inter-frame context state tree determined by the context state tree determination module 702 and the predicted value determined by the prediction module 703. In some embodiments, when the prediction mode is an intra-frame prediction mode, the context probability model group 704 is configured to encode the symbol to be encoded based on the intra-frame context state tree.

[0192] In some embodiments, the encoding module may include a context probability model group 704. The context probability model group 704 may include one or more context probability model groups 704-1, 704-2, ..., 704-H, where H = 13. In some embodiments, H may also be determined based on the number of bits of position information in the vertex to be encoded. In some embodiments, when the prediction mode is intra-frame prediction mode, the context probability model group 704-1 may be configured to determine the context probability model based on the intra-frame context state tree for subsequent encoding. In some embodiments, when the prediction mode is inter-frame prediction mode, the context probability model groups 704-2 and 704-3 may be configured to determine the context probability model based on inter-frame context state tree one, inter-frame context state tree two, and their corresponding predicted values, respectively. In some embodiments, when N is other values, the context probability model group 704 can set the corresponding inter-frame context state tree and the corresponding context probability model group according to the value of N, and determine the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group.

[0193] In some embodiments, the context probability model group 704 may include one or more context probability model groups 704-1, 704-2, ..., 704-H, where H = 7. In some embodiments, H may be determined based on the number of bits of position information in the vertex to be encoded. In some embodiments, when the prediction mode is an intra-frame prediction mode, the context probability model group 704-1 may be configured to determine the context probability model based on the intra-frame context state tree. In some embodiments, when the prediction mode is an inter-frame prediction mode, each of the inter-frame context state trees 1 and 2 corresponds to three context probability model groups 704-i, 704-i+1, and 704-i+2. That is, each inter-frame context state tree corresponds to three prediction values, and each prediction value corresponds to one context probability model group 704-i. For example, when the prediction mode is an inter-frame prediction mode and the context state tree is an inter-frame context state tree, the prediction value can be no prediction, prediction 0, and prediction 1, where no prediction corresponds to context probability model group 704-i; prediction 0 corresponds to context probability model group 704-i+1; and prediction 1 corresponds to context probability model group 704-i+1.

[0194] In some embodiments, the context probability model group 704 may include context probability model groups 1-7. When the context state tree is an inter-frame context state tree one, if the predicted value is no prediction, the context probability model group 704 may use context probability model group 704-2; if the predicted value is predicted 0, the context probability model group 704 may use context probability model group 704-3; and if the predicted value is predicted 1, the context probability model group 704 may use context probability model group 704-4. Similarly, when the context state tree is an inter-frame context state tree two, if the predicted value is no prediction, the context probability model group 704 may use context probability model group 704-5; if the predicted value is predicted 0, the context probability model group 704 may use context probability model group 704-6; and if the predicted value is predicted 1, the context probability model group 704 may use context probability model group 704-7. In the above cases, both the intra-frame context state tree and the inter-frame context state trees one and two may include 15 bits of information. The secondary information structure of the context state tree can be shown in Figure 9.

[0195] The context probability model group 704 uses seven context probability model groups to select a suitable context probability model group for each context state tree. The suitable context probability model group refers to the context probability model group corresponding to the predicted value of the context state tree. This approach avoids different predicted values ​​of the context state tree sharing the same context probability model group, thereby improving the efficiency of context probability model group probability updates and thus improving prediction performance.

[0196] In some embodiments, each context probability model group in the context probability model group 704 may include one or more encoders. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, the context probability model i indicated by the context probability model index can be determined.

[0197] In some embodiments, the context probability model group 704 may include context probability model groups 1-7. Based on the updated predicted value, the predicted value may include the following cases: 1. No prediction; 2. Predicted value was 0 before update, no prediction after update; 3. Predicted value was 1 before update, no prediction after update; 4. Predicted value was 0; 5. Predicted value was 1. For the above predicted values, based on the context state tree being inter-frame context state tree one, a predicted value of "not predicted" corresponds to context probability model group 704-2 in context probability model group 704. That is, when the predicted value is "not predicted," inter-frame context state tree one corresponds to context probability model group 704-2 in context probability model group 704. A predicted value of 0 before the update and "not predicted" after the update corresponds to context probability model group 704-3 in context probability model group 704. That is, when the predicted value is 0 before the update and "not predicted" after the update, inter-frame context state tree one corresponds to context probability model group 704-3 in context probability model group 704. A predicted value of 1 before the update and "not predicted" after the update corresponds to context probability model group 704-3. In context probability model group 704-4 of model group 704, when the predicted value is 1 before the update and no prediction after the update, the inter-frame context state tree 1 corresponds to context probability model group 704-4 in context probability model group 704; when the predicted value is 0, the inter-frame context state tree 1 corresponds to context probability model group 704-3 in context probability model group 704; when the predicted value is 1, the inter-frame context state tree 1 corresponds to context probability model group 704-4 in context probability model group 704.

[0198] Similarly, for the above predicted values, based on the context state tree being inter-frame context state tree two, a predicted value of "not predicted" corresponds to context probability model group 704-5 in context probability model group 704. That is, when the predicted value is "not predicted," the inter-frame context state tree two corresponds to context probability model group 704-5 in context probability model group 704. A predicted value of 0 before the update and "not predicted" after the update corresponds to context probability model group 704-6 in context probability model group 704. That is, when the predicted value is 0 before the update and "not predicted" after the update, the inter-frame context state tree two corresponds to context probability model group 704-6 in context probability model group 704. A predicted value of 1 before the update and "not predicted" after the update corresponds to context probability model group 704-6. In the context probability model group 704-7 of the probability model group 704, when the predicted value is 1 before the update and no prediction after the update, the second inter-frame context state tree corresponds to the context probability model group 704-7 in the context probability model group 704; when the predicted value is 0, it corresponds to the context probability model group 704-6 in the context probability model group 704; when the predicted value is 1, it corresponds to the context probability model group 704-7 in the context probability model group 704; when the predicted value is 1, it corresponds to the context probability model group 704-7 in the context probability model group 704. For the above situations, the secondary information of the intra-frame context state tree includes 15 bits of information, and the first, second, third, and fourth inter-frame context state trees can each include 16 bits of information. The structure of the secondary information of the context state tree can be as shown in Figure 10.

[0199] In some embodiments, the encoding module 700 can be configured not only to encode the second bit in the position information of the vertex to be encoded, but also to encode any bit in the position information of the vertex to be encoded.

[0200] Figure 11 shows an exemplary block diagram of a decoding module 1100 that can be configured to perform Trisoup geometric decoding based on inter-frame prediction according to some embodiments of this application. In some embodiments, the decoding module 1100 can be configured to encode a second bit of the position information in the vertex to be decoded.

[0201] In some embodiments, the decoding module 1100 may include a prediction module 1103. The prediction module 1103 may be configured to determine the predicted value of the symbol to be decoded when the prediction mode is inter-frame prediction mode. In some embodiments, the prediction module 1103 includes one or more prediction units 1103-1, 1103-2, ..., 1103-G, where G = 2. In some embodiments, G may be determined based on the number of bits of position information in the vertex to be encoded. In some embodiments, each of the one or more prediction units has the same structure.

[0202] In some embodiments, the decoding module 1100 may include a context probability model group 1104. The context probability model group 1104 determines a context probability model based on the inter-frame context state tree determined by the context state tree determination module 1102 and the predicted value determined by the prediction module 1103, which can be configured to subsequently decode the symbol to be decoded. In some embodiments, when the prediction mode is an intra-frame prediction mode, the context probability model group 1103 determines the context probability model based on the intra-frame context state tree. In some embodiments, the context probability model group 1104 may include one or more context probability model groups 1104-1, 1104-2, ..., 1104-H, where H = 7. In some embodiments, H may be determined based on the number of bits of position information in the vertex to be encoded. In some embodiments, the context probability model group 1104 performs a decoding operation corresponding to the encoding operation performed by the context probability model group 704 as described in conjunction with FIG. 7; repeated details are not described here.

[0203] In some embodiments, the decoding module 1100 can be configured not only to decode the second bit in the position information of the vertex to be decoded, but also to decode any bit in the position information of the vertex to be decoded.

[0204] Figure 12 shows an exemplary flowchart of an encoding method 1200 using dynamically updated optimal binarization techniques according to some embodiments of this application. In some embodiments, the entity executing the encoding method may be an encoder. The encoder may encode the second bit in the position information of the vertex to be encoded. The second bit is the symbol to be encoded.

[0205] Figure 16 shows an exemplary flowchart of an encoding method 1600 based on inter-frame prediction Trisoup geometric coding according to some embodiments of this application. In some embodiments, the entity executing the encoding method may be an encoder. The encoder may be and / or a combination of the G-PCC encoder 116, the geometric coding module 210, and the coding module 503 described in Figures 1, 2, and 5. The encoder may encode the second bit in the position information of the vertex to be encoded. The second bit is the symbol to be encoded.

[0206] As shown in Figure 16, in step 1601, the encoder can determine the prediction mode based on a first syntax element and compensation reference vertex information. The first syntax element can be configured to indicate whether inter-frame prediction is enabled, and the compensation reference vertex information can be configured to indicate whether a compensation reference vertex for the vertex to be encoded exists after enabling inter-frame prediction mode. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled, or when the compensation reference vertex information can be configured to indicate that a compensation reference vertex for the vertex to be encoded does not exist after enabling inter-frame prediction mode, the encoder can determine that the prediction mode is an intra-frame prediction mode; that is, the encoding method flow can proceed to step 1602. In some embodiments, based on the first syntax element indicating that inter-frame prediction is enabled and the compensation reference vertex information being configured to indicate that a compensation reference vertex for the vertex to be encoded exists after enabling inter-frame prediction mode, the encoder can determine that the prediction mode is an inter-frame prediction mode; that is, the encoding method flow can proceed to step 1603.

[0207] In step 1602, the encoder determines that the prediction mode is an intra-frame prediction mode, and the encoder determines that the context state tree is an intra-frame context state tree.

[0208] In 1603, the encoder determines that the prediction mode is an inter-frame prediction mode.

[0209] In 1604, the encoder can determine the inter-frame context state tree based on the parity bit information of the compensation reference vertex information. In some embodiments, when the encoder determines that the prediction mode is an inter-frame prediction mode, the encoder can determine the inter-frame context state tree based on the parity bit information of the compensation reference vertex information. In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one and inter-frame context state tree two. In some embodiments, when the parity bit information of the compensation reference vertex information is 0, the encoder can determine that the inter-frame context state tree is inter-frame context state tree one. When the parity bit information of the compensation reference vertex information is 1, the encoder can determine that the inter-frame context state tree is inter-frame context state tree two.

[0210] In 1605, the encoder can determine the predicted value of the symbol to be encoded. In some embodiments, the encoder determines the predicted value based on the co-occurrence bit information of the uncompensated reference vertex information of the vertex to be encoded, wherein the value of the second syntax element is determined based on the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex, the uncompensated reference vertex information, and the prediction result of the first bit of the uncompensated reference vertex. In some embodiments, the value of the second syntax element is not equal to 1 if the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex does not meet a preset condition, or if the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist, or if the prediction result of the first bit of the uncompensated reference vertex does not meet a preset condition; the value of the second syntax element is equal to 1 if the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex meets a preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the first bit of the uncompensated reference vertex meets a preset condition. In some embodiments, the preset condition refers to a good prediction result for predicting the second bit of a neighboring vertex based on the uncompensated neighboring reference vertex or a good prediction result for predicting the first bit of an uncompensated neighboring reference vertex, that is, a high prediction accuracy for predicting the second bit of a neighboring vertex based on the uncompensated neighboring reference vertex or a good prediction result for predicting the first bit of an uncompensated neighboring reference vertex.

[0211] In some embodiments, based on the fact that the value of the second syntax element is not equal to 1, the encoder can determine that the symbol to be encoded will not be predicted using uncompensated reference vertex information, i.e., no prediction (NoPred). In some embodiments, based on the fact that the value of the second syntax element is equal to 1, the encoder can determine the predicted value of the symbol to be encoded based on the parity bit information of the uncompensated reference vertex information of the vertex to be encoded. Based on the factity bit information of the uncompensated reference vertex information of the vertex to be encoded being 0, the encoder can determine that the predicted value of the symbol to be encoded is 0 (i.e., Pred 0). Based on the factity bit information of the uncompensated reference vertex information of the vertex to be encoded being 1, the encoder can determine that the predicted value of the symbol to be encoded is 1 (i.e., Pred 1).

[0212] In some embodiments, based on the predicted value of the symbol to be encoded being 0 or 1, the encoder can update the predicted value of the symbol to be encoded based on a first quantity information and a second quantity information, wherein the first quantity information is the cumulative sum of the second bits of the encoded vertex position information that were judged to be located, and the second quantity information is the cumulative sum of the second bits of the encoded vertex position information. In some embodiments, the encoder can update the predicted value of the symbol to be encoded based on whether the ratio of the first quantity information and the second quantity information is greater than a preset threshold. In some embodiments, based on the ratio of the first quantity information and the second quantity information not being greater than the preset threshold, the encoder can update the predicted value of the symbol to be encoded to no prediction; that is, when the predicted value is 0 or 1, based on the ratio of the first quantity information and the second quantity information not being greater than the preset threshold, the predicted value is updated to no prediction. In some embodiments, based on the ratio of the first quantity information and the second quantity information being greater than the preset threshold, the encoder does not update the predicted value; that is, when the predicted value is 0 or 1, based on the ratio of the first quantity information and the second quantity information being greater than the preset threshold, the predicted value remains the original predicted value of 0 or 1. In some embodiments, the predicted value determined by the encoder may include the following cases: 1. No prediction; 2. Predicted value is 0 before update, and no prediction after update; 3. Predicted value is 1 before update, and no prediction after update; 4. Predicted value is 0; 5. Predicted value is 1.

[0213] In 1606, the encoder can determine, based on the context state tree and the predicted value, that a context probability model is configured to encode the symbol to be encoded. In some embodiments, a context probability model index is determined based on the inter-frame context state tree and the context probability model group, the context probability model group may include one or more encoders, the context probability model index indicating one of the encoders in the context probability model group; the context probability model indicated by the context probability model index is determined based on the inter-frame context state tree. In some embodiments, when the prediction mode is an intra-frame prediction mode, the encoder determines, based on the intra-frame context state tree, that a context probability model is configured to encode the symbol to be encoded.

[0214] In some embodiments, when the prediction mode is an inter-frame prediction mode, the encoder determines the context probability model based on inter-frame context state tree one, inter-frame context state tree two, and their corresponding predicted values, respectively. In some embodiments, the encoder may include one or more context probability model groups 1-H, where H=3. Context probability model group 1 corresponds to the intra-frame context state tree; context probability model group 2 corresponds to inter-frame context state tree one; and context probability model group 3 corresponds to inter-frame context state tree two.

[0215] In some embodiments, the encoder may include one or more context probability model groups 1-H, where H = 7. In some embodiments, H may also be determined based on the number of bits of position information in the vertex to be encoded. Specifically, context probability model group 1 corresponds to the intra-frame context state tree; context probability model group 2 corresponds to inter-frame context state tree one and the predicted value is not predicted; context probability model group 3 corresponds to inter-frame context state tree one and the predicted value is 0; context probability model group 4 corresponds to inter-frame context state tree one and the predicted value is 1; context probability model group 5 corresponds to inter-frame context state tree two and the predicted value is not predicted; context probability model group 6 corresponds to inter-frame context state tree two and the predicted value is 0; and context probability model group 7 corresponds to inter-frame context state tree two and the predicted value is 1.

[0216] In some embodiments, the encoder may include one or more context probability model groups 1-H, where H = 7. In some embodiments, H may also be determined based on the number of bits of position information in the vertex to be encoded. In some embodiments, the predicted value determined by the encoder may include the following cases: 1. No prediction; 2. Predicted value is 0 before update, no prediction after update; 3. Predicted value is 1 before update, no prediction after update; 4. Predicted value is 0; 5. Predicted value is 1. Wherein, based on the context state tree being an intra-frame context state tree, the encoder uses context probability model group 1; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction, the encoder uses context probability model group 2; based on the context state tree being an inter-frame context state tree and the predicted value being 0 before the update and no prediction after the update, the encoder uses context probability model group 3; based on the context state tree being an inter-frame context state tree and the predicted value being 1 before the update and no prediction after the update, the encoder uses context probability model group 4; based on the context state tree being an inter-frame context state tree and the predicted value being 0 ... no prediction after the update, the encoder uses context probability model group 4; based on the context state tree being an inter-frame context state tree and the predicted value being 0, the encoder uses context probability model group 3; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the encoder uses context probability model group 4; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the encoder uses context probability model group 5; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the encoder uses context probability model group 6; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the encoder uses context probability model group 7; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the encoder uses context probability model group 8; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after If the value is 1, the encoder uses context probability model group 4; if the context state tree is an inter-frame context state tree II and the predicted value is no prediction, the encoder uses context probability model group 5; if the context state tree is an inter-frame context state tree II and the predicted value is 0 before the update and no prediction after the update, the encoder uses context probability model group 6; if the context state tree is an inter-frame context state tree II and the predicted value is 1 before the update and no prediction after the update, the encoder uses context probability model group 7; if the context state tree is an inter-frame context state tree II and the predicted value is 0, the encoder uses context probability model group 6; if the context state tree is an inter-frame context state tree II and the predicted value is 1, the encoder uses context probability model group 7.

[0217] In some embodiments, the context probability model group may include one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, the encoder may determine the context probability model i indicated by the context probability model index.

[0218] In 1607, the encoder encodes the symbol to be encoded based on the context probability model.

[0219] In some embodiments, the encoding method can be configured not only to encode the second bit in the position information of the vertex to be encoded, but also to encode any bit in the position information of the vertex to be encoded.

[0220] In some embodiments, the encoding method described in this application can merge some inter-frame context state trees, avoiding the problem that when inter-frame prediction is used less frequently, the probabilities corresponding to the inter-frame context state trees converge slowly, resulting in poor prediction performance. Furthermore, the encoding method described in this application can also prevent different predicted values ​​from sharing the same context probability model group, thereby improving the efficiency of probability updates for the context probability model group.

[0221] Figure 17 shows an exemplary flowchart of a decoding method 1700 based on inter-frame prediction Trisoup geometric decoding according to some embodiments of this application. In some embodiments, the execution entity of the decoding method may be a decoder. The decoder may decode the second bit in the position information of the vertex to be decoded. The second bit is the symbol to be decoded.

[0222] As shown in Figure 17, in step 1701, the decoder can determine the prediction mode based on a first syntax element and compensation reference vertex information. The first syntax element can be configured to indicate whether inter-frame prediction is enabled, and the compensation reference vertex information can be configured to indicate whether a compensation reference vertex for the vertex to be decoded exists after enabling inter-frame prediction mode. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled, or when the compensation reference vertex information can be configured to indicate that a compensation reference vertex for the vertex to be decoded does not exist after enabling inter-frame prediction mode, the decoder can determine that the prediction mode is an intra-frame prediction mode; that is, the decoding method flow can proceed to step 1702. In some embodiments, based on the first syntax element indicating that inter-frame prediction is enabled and the compensation reference vertex information being configured to indicate that a compensation reference vertex for the vertex to be decoded exists after enabling inter-frame prediction mode, the decoder can determine that the prediction mode is an inter-frame prediction mode; that is, the decoding method flow can proceed to step 1703.

[0223] In 1702, the decoder determines that the prediction mode is an intra-frame prediction mode, and the decoder determines that the context state tree is an intra-frame context state tree.

[0224] In 1703, the decoder determines that the prediction mode is an inter-frame prediction mode.

[0225] In 1704, the decoder can determine the inter-frame context state tree based on the parity bit information of the compensation reference vertex information. In some embodiments, when the decoder determines that the prediction mode is an inter-frame prediction mode, the decoder can determine the inter-frame context state tree based on the parity bit information of the compensation reference vertex information. In some embodiments, the inter-frame context state tree can be divided into inter-frame context state tree one and inter-frame context state tree two. In some embodiments, when the parity bit information of the compensation reference vertex information is 0, the decoder can determine that the inter-frame context state tree is inter-frame context state tree one. When the parity bit information of the compensation reference vertex information is 1, the decoder can determine that the inter-frame context state tree is inter-frame context state tree two.

[0226] In 1705, the decoder can determine the predicted value of the symbol to be decoded. In some embodiments, the decoder determines the predicted value based on the co-occurrence bit information of the uncompensated reference vertex information of the vertex to be encoded, wherein the value of the second syntax element is determined based on the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex, the uncompensated reference vertex information, and the prediction result of the first bit of the uncompensated reference vertex. In some embodiments, the value of the second syntax element is not equal to 1 if the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex does not meet a preset condition, or if the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist, or if the prediction result of the first bit of the uncompensated reference vertex does not meet a preset condition; the value of the second syntax element is equal to 1 if the prediction result of the second bit of the neighboring uncompensated reference vertex predicting the neighboring vertex meets a preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the first bit of the uncompensated reference vertex meets a preset condition. In some embodiments, the preset condition refers to a good prediction result for predicting the second bit of a neighboring vertex based on the uncompensated neighboring reference vertex or a good prediction result for predicting the first bit of an uncompensated neighboring reference vertex, that is, a high prediction accuracy for predicting the second bit of a neighboring vertex based on the uncompensated neighboring reference vertex or a good prediction result for predicting the first bit of an uncompensated neighboring reference vertex.

[0227] In some embodiments, based on the fact that the value of the second syntax element is not equal to 1, the decoder can determine that the symbol to be decoded is not predicted using uncompensated reference vertex information, i.e., no prediction (NoPred). In some embodiments, based on the fact that the value of the second syntax element is equal to 1, the decoder can determine the predicted value of the symbol to be decoded based on the parity bit information of the uncompensated reference vertex information of the vertex to be encoded. Based on the factity bit information of the uncompensated reference vertex information of the vertex to be encoded being 0, the decoder can determine that the predicted value of the symbol to be decoded is 0 (Pred 0). Based on the factity bit information of the uncompensated reference vertex information of the vertex to be encoded being 1, the decoder can determine that the predicted value of the symbol to be decoded is 1 (Pred 1).

[0228] In some embodiments, based on the predicted value of the symbol to be decoded being 0 or 1, the decoder can update the predicted value of the symbol to be decoded based on a sixth quantity information and a seventh quantity information, wherein the sixth quantity information is the cumulative sum of the second bits of the decoded vertex position information that were judged to be located, and the second quantity information is the cumulative sum of the second bits of the decoded vertex position information. In some embodiments, the decoder can update the predicted value of the symbol to be decoded based on whether the ratio of the sixth quantity information and the seventh quantity information is greater than a preset threshold. In some embodiments, based on the ratio of the sixth quantity information and the seventh quantity information not being greater than the preset threshold, the decoder can update the predicted value of the symbol to be decoded to no prediction; that is, when the predicted value is 0 or 1, based on the ratio of the sixth quantity information and the seventh quantity information not being greater than the preset threshold, the predicted value is updated to no prediction. In some embodiments, based on the ratio of the sixth quantity information and the seventh quantity information being greater than the preset threshold, the decoder does not update the predicted value; that is, when the predicted value is 0 or 1, based on the ratio of the sixth quantity information and the seventh quantity information being greater than the preset threshold, the predicted value remains the original predicted value of 0 or 1. In some embodiments, the predicted value determined by the decoder may include the following cases: 1. No prediction; 2. Predicted value is 0 before update, and no prediction after update; 3. Predicted value is 1 before update, and no prediction after update; 4. Predicted value is 0; 5. Predicted value is 1.

[0229] In 1706, the decoder can determine, based on the context state tree and the predicted value, that a context probability model is configured to decode the symbol to be decoded. In some embodiments, when the prediction mode is an intra-frame prediction mode, the decoder can determine, based on the intra-frame context state tree, that a context probability model is configured to decode the symbol to be decoded.

[0230] In some embodiments, when the prediction mode is an inter-frame prediction mode, the decoder determines the context probability model based on inter-frame context state tree one, inter-frame context state tree two, and their corresponding predicted values, respectively. In some embodiments, the decoder may include one or more context probability model groups 1-H, where H=3. Context probability model group 1 corresponds to the intra-frame context state tree; context probability model group 2 corresponds to inter-frame context state tree one; and context probability model group 3 corresponds to inter-frame context state tree two.

[0231] In some embodiments, the decoder may include one or more context probability model groups 1-H, where H = 7. In some embodiments, H may also be determined based on the number of bits of position information in the vertex to be decoded. Specifically, context probability model group 1 corresponds to the intra-frame context state tree; context probability model group 2 corresponds to inter-frame context state tree one and the predicted value is not predicted; context probability model group 3 corresponds to inter-frame context state tree one and the predicted value is 0; context probability model group 4 corresponds to inter-frame context state tree one and the predicted value is 1; context probability model group 5 corresponds to inter-frame context state tree two and the predicted value is not predicted; context probability model group 6 corresponds to inter-frame context state tree two and the predicted value is 0; and context probability model group 7 corresponds to inter-frame context state tree two and the predicted value is 1.

[0232] In some embodiments, the decoder may include one or more context probability model groups 1-H, where H = 7. In some embodiments, H may also be determined based on the number of bits of position information in the vertex to be decoded. In some embodiments, the predicted value determined by the decoder may include the following cases: 1. No prediction; 2. Predicted value is 0 before update, no prediction after update; 3. Predicted value is 1 before update, no prediction after update; 4. Predicted value is 0; 5. Predicted value is 1. Wherein, based on the context state tree being an intra-frame context state tree, the decoder uses context probability model group 1; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction, the decoder uses context probability model group 2; based on the context state tree being an inter-frame context state tree and the predicted value being 0 before the update and no prediction after the update, the decoder uses context probability model group 3; based on the context state tree being an inter-frame context state tree and the predicted value being 1 before the update and no prediction after the update, the decoder uses context probability model group 4; based on the context state tree being an inter-frame context state tree and the predicted value being 0 ... no prediction after the update, the decoder uses context probability model group 4; based on the context state tree being an inter-frame context state tree and the predicted value being 0, the decoder uses context probability model group 3; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the decoder uses context probability model group 4; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the decoder uses context probability model group 5; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the decoder uses context probability model group 6; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the decoder uses context probability model group 7; based on the context state tree being an inter-frame context state tree and the predicted value being no prediction after the update, the decoder uses context probability model group 8; based on the context state tree being an If the value is 1, the decoder uses context probability model group 4; if the context state tree is an inter-frame context state tree II and the predicted value is not predicted, the decoder uses context probability model group 5; if the context state tree is an inter-frame context state tree II and the predicted value is 0 before the update and not predicted after the update, the decoder uses context probability model group 6; if the context state tree is an inter-frame context state tree II and the predicted value is 1 before the update and not predicted after the update, the decoder uses context probability model group 7; if the context state tree is an inter-frame context state tree II and the predicted value is 0, the decoder uses context probability model group 6; if the context state tree is an inter-frame context state tree II and the predicted value is 1, the decoder uses context probability model group 7.

[0233] In some embodiments, each of the context probability model groups may include one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines the corresponding context probability model index based on a determined context state tree, wherein the context probability model index may indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, the context probability model i indicated by the context probability model index can be determined.

[0234] In 1707, the decoder decodes the symbol to be decoded based on the context probability model.

[0235] In some embodiments, the decoding method can be configured not only to decode the second bit in the position information of the vertex to be decoded, but also to decode any bit in the position information of the vertex to be decoded.

[0236] In some embodiments, some embodiments of this application also provide a non-volatile storage medium that can be configured to store computer-readable instructions for implementing the encoding and / or decoding methods provided in the embodiments of this application. When the computer-readable instructions are invoked by a processor, the processor can be configured to execute the encoding and / or decoding methods provided in the embodiments of this application.

[0237] In some embodiments, this application also provides a chip, which may include a memory configured to store computer-readable instructions capable of executing the encoding and / or decoding methods provided in the embodiments of this application. In the embodiments of this application, the chip may include a processor. When the computer-readable instructions are invoked by the processor, the processor may be configured to execute and / or implement the encoding and / or decoding methods provided in the embodiments of this application.

[0238] It should be noted that the various modules provided in some embodiments of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the hardware structure's processor and / or controller in hardware form, or stored in the hardware structure's memory in software form, so that the processor and / or controller can call and execute the operations corresponding to these modules.

[0239] For the sake of brevity, the method of this application is depicted and described as a series of actions. However, the actions according to this application can occur in various sequences and / or simultaneously, and together with other actions not proposed or described in this application. Furthermore, not all described actions can be claimed to implement the method according to the disclosed subject matter. In addition, those skilled in the art will understand and recognize that the method can alternatively be represented as a series of mutual states via state diagrams or events.

[0240] This application has mentioned many details in the foregoing description. However, it is obvious that this application can be implemented without these specific details. In some examples, well-known structures and devices are shown in block diagrams rather than in specific details in order to highlight the content of this application.

[0241] The terms “first,” “second,” “third,” “fourth,” etc., used in this article are labels that can be configured to distinguish different elements and do not necessarily have the ordinal meaning of the numerical designation used.

[0242] While it will be obvious to those skilled in the art, upon understanding the foregoing description, that other changes and modifications to the content of this application will be apparent, it should be understood that any specific embodiments shown and described in the illustrative manner should not be considered limiting. Therefore, the details of the various embodiments are not intended to limit the scope of the claims, which themselves merely set forth the disclosed technical features.

Claims

1. A coding method based on inter-frame prediction, comprising: Determine the prediction mode of the symbol to be encoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode; Based on the prediction mode being an inter-frame prediction mode, the inter-frame context state tree of the symbol to be encoded is determined; Based on the inter-frame context state tree, determine the predicted value of the symbol to be encoded; Based on the inter-frame context state tree and the predicted value, the context probability model group corresponding to the symbol to be encoded is determined, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; The context probability model is determined based on the context probability model group corresponding to the symbol to be encoded; The symbol to be encoded is encoded based on the context probability model.

2. The method according to claim 1, wherein, The symbol to be encoded indicates the position information of the vertex to be encoded. The position information is represented by N bits, where N is an integer greater than or equal to 1. Each bit in the position information divides the position interval determined by the preceding bits into two intervals, left and right. Alternatively, where N is an integer greater than or equal to 2, the first bit in the position information indicates that the position interval where the vertex is located is divided into a left interval and a right interval. The N-1 bits in the position information other than the first bit indicate that the left interval or the right interval is divided into m position intervals, where m is an integer and m is less than or equal to 2 to the power of N-1.

3. The method according to claim 1, wherein, The prediction mode for determining the symbol to be encoded includes: A prediction mode is determined based on a first syntax element and uncompensated reference vertex information, wherein the first syntax element is configured to indicate whether inter-frame prediction is enabled, and the uncompensated reference vertex information is configured to indicate whether uncompensated reference vertices exist.

4. The method according to claim 3, wherein, The process of determining the prediction mode based on the first syntax element and the uncompensated reference vertex information includes: Based on the first syntax element indicating that inter-frame prediction is not enabled or the uncompensated reference vertex information indicating that there are no uncompensated reference vertices, the prediction mode is determined to be intra-frame prediction mode. Based on the first syntax element indicating that inter-frame prediction is enabled and the uncompensated reference vertex information indicating the existence of uncompensated reference vertices, the prediction mode is determined based on the first quantity information and the second quantity information, wherein the first quantity information indicates the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit of the neighboring vertex, and the second quantity information indicates the number of inaccurate predictions when the neighboring compensated reference vertex predicts the first bit of the neighboring vertex.

5. The method according to claim 4, wherein, Determining the prediction pattern based on the first quantity information and the second quantity information includes: Based on the first quantity information being less than or equal to the first threshold or the second quantity information being less than or equal to the second threshold, the prediction mode is determined to be an inter-frame prediction mode. Based on the fact that the first quantity information is greater than the first threshold and the second quantity information is greater than the second threshold, the prediction mode is determined to be an intra-frame prediction mode.

6. The method according to claim 1, wherein, The method includes: Based on the fact that the prediction mode is an intra-frame prediction mode, the context state tree is determined to be an intra-frame context state tree.

7. The method according to claim 2, wherein, The process of determining the inter-frame context state tree of the symbol to be encoded includes: Based on the first indication information, the inter-frame context state tree is determined, wherein the first indication information indicates the position information of the compensation reference vertex.

8. The method according to claim 7, wherein, The inter-frame context state tree includes: Inter-frame context state tree 1, inter-frame context state tree 2, inter-frame context state tree 3, and inter-frame context state tree 4; the values ​​of the first indication information include: 0, 1, 2, 3, and 4.

9. The method according to claim 8, wherein, Determining the inter-frame context state tree based on the first indication information includes: Based on the value of 0 of the first indication information, the prediction mode is determined to be an intra-frame prediction mode, and the inter-frame context state tree is an intra-frame context state tree. Based on the value of 1 in the first indication information, the inter-frame context is determined to be inter-frame context state tree one. Based on the value of 2 in the first indication information, the inter-frame context is determined to be inter-frame context state tree two. Based on the value of 3 in the first indication information, the inter-frame context is determined to be inter-frame context state tree three. Based on the value of 4 in the first indication information, the inter-frame context is determined to be inter-frame context state tree four.

10. The method according to claim 9, wherein, Determining the predicted value of the symbol to be encoded includes: The predicted value is determined based on the third quantity information and the uncompensated reference vertex information, wherein the third quantity information indicates the number of times the neighbor uncompensated reference vertex makes an inaccurate prediction when predicting the second bit in the neighbor vertex information.

11. The method according to claim 10, wherein, The determination of the predicted value based on the third quantity information and the uncompensated reference vertex information includes: If the number of inaccurate predictions when predicting the second bit of the neighbor vertex information based on the third quantity information indicates that the number of inaccurate predictions is greater than the first threshold, or if the uncompensated reference vertex information indicates that there is no uncompensated reference vertex, the prediction value is determined to be no prediction. Based on the third quantity information indicating that the number of inaccurate predictions when predicting the second bit of the neighbor uncompensated reference vertex information is less than or equal to the first threshold, and the uncompensated reference vertex information indicating the existence of an uncompensated reference vertex, the predicted value is determined based on the first bit of the position information of the uncompensated reference vertex.

12. The method according to claim 11, wherein, Determining the predicted value based on the first bit in the position information of the uncompensated reference vertex includes: Based on the fact that the first bit in the position information of the uncompensated reference vertex is 0, the predicted value is determined to be 0; Based on the fact that the first bit in the position information of the uncompensated reference vertex is 1, the predicted value is determined to be 1.

13. The method according to claim 12, wherein, The context probability model group includes context probability model group 1 to context probability model group 13; wherein, determining the context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group eight. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group nine. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 10. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 11. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 12. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 13.

14. The method according to claim 12, wherein, The method includes: Based on the predicted value being 0 or the predicted value being 1, the predicted value is updated based on the ratio of the fourth quantity information and the fifth quantity information, wherein the fourth quantity information indicates that the first bit in the encoded vertex position information is determined to be the cumulative sum of the predictions, and the fifth quantity information indicates the cumulative sum of the first bit in the decoded vertex position information.

15. The method according to claim 14, wherein, The prediction value is updated based on the ratio of the fourth and fifth quantity information, including: Based on the fact that the ratio of the fourth quantity information and the fifth quantity information is less than or equal to a preset threshold, the predicted value is updated to not predict. If the ratio of the fourth quantity information to the fifth quantity information is greater than a preset threshold, the predicted value will not be updated.

16. The method according to claim 15, wherein, The predicted value includes any of the following: No prediction; The predicted value before the update is 0, and the predicted value after the update is no prediction. The predicted value before the update is 1, and the predicted value after the update is no prediction. The predicted value is 0; The predicted value is 1.

17. The method according to claim 16, wherein, The context probability model group includes context probability model group 1 to context probability model group 13; wherein, determining the context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 4. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group eight. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group nine. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 10. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group nine. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 10. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the predicted value being... Prediction determines that the context probability model group corresponding to the symbol to be encoded is context probability model group 11; Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 12. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 13. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 12. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 13.

18. The method according to any one of claims 1-13, wherein, The inter-frame context state tree includes primary information and secondary information, with the secondary information comprising 15 bits of information.

19. The method according to any one of claims 1-12 or 14-17, wherein, The inter-frame context state tree includes primary information and secondary information, and the secondary information includes 16 bits of information.

20. The method according to claim 1, wherein, The determination of the context probability model based on the context probability model group corresponding to the symbol to be encoded includes: Based on the inter-frame context state tree and the context probability model group, a context probability model index is determined. The context probability model group includes one or more context probability models, and the context probability model index indicates a context probability model in the context probability model group. The context probability model indicated by the context probability model index is determined based on the inter-frame context state tree.

21. The method according to any one of claims 1-20, wherein, The symbol to be encoded is the first bit in the location information.

22. An encoder based on inter-frame prediction, comprising: The prediction mode determination module determines the prediction mode of the symbol to be encoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode. The context state tree determination module is configured to determine the inter-frame context state tree of the symbol to be encoded based on the prediction mode being an inter-frame prediction mode. The prediction module is configured to determine the predicted value of the symbol to be encoded based on the inter-frame context state tree; A context probability model group is determined based on the inter-frame context state tree and the predicted value to determine the context probability model group corresponding to the symbol to be encoded, and a context probability model is determined based on the context probability model group corresponding to the symbol to be encoded, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; The entropy coding module is configured to encode the symbol to be encoded based on the context probability model.

23. The encoder according to claim 22, wherein, The symbol to be encoded is the first bit in the location information.

24. A decoding method based on inter-frame prediction, comprising: Determine the prediction mode of the symbol to be decoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode; Based on the prediction mode being an inter-frame prediction mode, the inter-frame context state tree of the symbol to be decoded is determined; Based on the inter-frame context state tree, determine the predicted value of the symbol to be decoded; Based on the inter-frame context state tree and the predicted value, the context probability model group corresponding to the symbol to be decoded is determined, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; Based on the context probability model group corresponding to the symbol to be decoded, determine the context probability model; The symbol to be decoded is decoded based on the context probability model.

25. The method according to claim 24, wherein, The symbol to be decoded indicates the position information of the vertex to be decoded. The position information is represented by N bits, where N is an integer greater than or equal to 1. Each bit in the position information divides the position interval determined by the preceding bits into two intervals, left and right. Alternatively, where N is an integer greater than or equal to 2, the first bit in the position information indicates that the position interval where the vertex is located is divided into a left interval and a right interval, and the N-1 bits in the position information other than the first bit indicate that the left interval or the right interval is divided into m position intervals, where m is an integer and m is less than or equal to 2 to the power of N-1.

26. The method according to claim 24, wherein, The prediction mode for determining the symbol to be decoded includes: A prediction mode is determined based on a first syntax element and uncompensated reference vertex information, wherein the first syntax element is configured to indicate whether inter-frame prediction is enabled, and the uncompensated reference vertex information is configured to indicate whether uncompensated reference vertices exist.

27. The method according to claim 26, wherein, The process of determining the prediction mode based on the first syntax element and uncompensated reference vertex information includes: Based on the first syntax element indicating that inter-frame prediction is not enabled or the uncompensated reference vertex information indicating that there are no uncompensated reference vertices, the prediction mode is determined to be intra-frame prediction mode. Based on the first syntax element indicating that inter-frame prediction is enabled and the uncompensated reference vertex information indicating the existence of uncompensated reference vertices, the prediction mode is determined based on the first quantity information and the second quantity information, wherein the first quantity information indicates the number of inaccurate predictions when the neighboring uncompensated reference vertex predicts the second bit of the neighboring vertex, and the second quantity information indicates the number of inaccurate predictions when the neighboring compensated reference vertex predicts the first bit of the neighboring vertex.

28. The method according to claim 27, wherein, Determining the prediction pattern based on the first quantity information and the second quantity information includes: Based on the first quantity information being less than or equal to the first threshold or the second quantity information being less than or equal to the second threshold, the prediction mode is determined to be an inter-frame prediction mode. Based on the fact that the first quantity information is greater than the first threshold and the second quantity information is greater than the second threshold, the prediction mode is determined to be an intra-frame prediction mode.

29. The method according to claim 24, wherein, The method includes: Based on the fact that the prediction mode is an intra-frame prediction mode, the context state tree is determined to be an intra-frame context state tree.

30. The method according to claim 25, wherein, The process of determining the inter-frame context state tree of the symbol to be decoded includes: Based on the first indication information, the inter-frame context state tree is determined, wherein the first indication information indicates the position information of the compensation reference vertex.

31. The method according to claim 30, wherein, The inter-frame context state tree includes: Inter-frame context state tree 1, inter-frame context state tree 2, inter-frame context state tree 3, and inter-frame context state tree 4; the values ​​of the first indication information include: 0, 1, 2, 3, and 4.

32. The method according to claim 31, wherein, Determining the inter-frame context state tree based on the first indication information includes: Based on the value of 0 of the first indication information, the prediction mode is determined to be an intra-frame prediction mode, and the inter-frame context state tree is an intra-frame context state tree. Based on the value of 1 in the first indication information, the inter-frame context is determined to be inter-frame context state tree one. Based on the value of 2 in the first indication information, the inter-frame context is determined to be inter-frame context state tree two. Based on the value of 3 in the first indication information, the inter-frame context is determined to be inter-frame context state tree three. Based on the value of 4 in the first indication information, the inter-frame context is determined to be inter-frame context state tree four.

33. The method according to claim 32, wherein, Determining the predicted value of the symbol to be decoded includes: The predicted value is determined based on the third quantity information and the uncompensated reference vertex information, wherein the third quantity information indicates the number of times the neighbor uncompensated reference vertex makes an inaccurate prediction when predicting the second bit in the neighbor vertex information.

34. The method according to claim 33, wherein, The determination of the predicted value based on the third quantity information and the uncompensated reference vertex information includes: If the number of inaccurate predictions when predicting the second bit of the neighbor vertex information based on the third quantity information indicates that the number of inaccurate predictions is greater than the first threshold, or if the uncompensated reference vertex information indicates that there is no uncompensated reference vertex, the prediction value is determined to be no prediction. Based on the third quantity information indicating that the number of inaccurate predictions when predicting the second bit of the neighbor uncompensated reference vertex information is less than or equal to the first threshold, and the uncompensated reference vertex information indicating the existence of an uncompensated reference vertex, the predicted value is determined based on the first bit of the position information of the uncompensated reference vertex.

35. The method according to claim 35, wherein, Determining the predicted value based on the first bit in the position information of the uncompensated reference vertex includes: Based on the fact that the first bit in the position information of the uncompensated reference vertex is 0, the predicted value is determined to be 0; Based on the fact that the first bit in the position information of the uncompensated reference vertex is 1, the predicted value is determined to be 1.

36. The method according to claim 35, wherein, The context probability model group includes context probability model group 1 to context probability model group 13; wherein, determining the context probability model group corresponding to the symbol to be decoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1. The context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 7; Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group eight. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group nine. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 10. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 11. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 12. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 13.

37. The method of claim 35, wherein, The method includes: Based on the predicted value being 0 or the predicted value being 1, the predicted value is updated based on the ratio of the fourth quantity information and the fifth quantity information, wherein the fourth quantity information indicates that the first bit in the position information of the decoded vertex is determined to be the cumulative sum of the predictions, and the fifth quantity information indicates the cumulative sum of the first bit in the position information of the decoded vertex.

38. The method according to claim 37, wherein, The prediction value is updated based on the ratio of the fourth and fifth quantity information, including: Based on the fact that the ratio of the fourth quantity information and the fifth quantity information is less than or equal to a preset threshold, the predicted value is updated to not predict. If the ratio of the fourth quantity information to the fifth quantity information is greater than a preset threshold, the predicted value will not be updated.

39. The method according to claim 38, wherein, The predicted value includes any of the following: No prediction; The predicted value before the update is 0, and the predicted value after the update is no prediction. The predicted value before the update is 1, and the predicted value after the update is no prediction. The predicted value is 0; The predicted value is 1.

40. The method according to claim 39, wherein, The context probability model group includes context probability model group 1 to context probability model group 13; wherein, determining the context probability model group corresponding to the symbol to be decoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 4. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group eight. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group nine. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 10. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group nine. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree three, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 10. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 11. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 12. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the predicted value being more... The new prediction value is 1, and the updated value is no prediction. The context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 13. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 12. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree four, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 13.

41. The method according to any one of claims 24-36, wherein, The inter-frame context state tree includes primary information and secondary information, with the secondary information comprising 15 bits of information.

42. The method according to any one of claims 24-40, wherein, The inter-frame context state tree includes primary information and secondary information, and the secondary information includes 16 bits of information.

43. The method according to claim 24, wherein, The determination of the context probability model based on the context probability model group corresponding to the symbol to be decoded includes: Based on the inter-frame context state tree and the context probability model group, a context probability model index is determined. The context probability model group includes one or more context probability models, and the context probability model index indicates a context probability model in the context probability model group. The context probability model indicated by the context probability model index is determined based on the inter-frame context state tree.

44. The method according to any one of claims 24-40, wherein, The symbol to be decoded is the first bit in the location information.

45. A decoder based on inter-frame prediction, comprising: The prediction mode determination module determines the prediction mode of the symbol to be decoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode. The context state tree determination module is configured to determine the inter-frame context state tree of the symbol to be decoded based on the prediction mode being an inter-frame prediction mode. The prediction module is configured to determine the predicted value of the symbol to be decoded for the inter-frame context state tree; A context probability model group is determined based on the inter-frame context state tree and the predicted value to determine the context probability model group corresponding to the symbol to be decoded, and a context probability model is determined based on the context probability model group corresponding to the symbol to be decoded, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; The entropy decoding module is configured to decode the symbol to be decoded based on the context probability model.

46. ​​The decoder according to claim 45, wherein, The symbol to be decoded is the first bit in the location information.

47. A coding method based on inter-frame prediction, comprising: Determine the prediction mode of the symbol to be encoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode; Based on the prediction mode being an inter-frame prediction mode, the inter-frame context state tree of the symbol to be encoded is determined; For the inter-frame context state tree, determine the predicted value of the symbol to be encoded; Based on the inter-frame context state tree and the predicted value, the context probability model group corresponding to the symbol to be encoded is determined, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; The context probability model is determined based on the context probability model group corresponding to the symbol to be encoded; The symbol to be encoded is encoded based on the context probability model.

48. The method according to claim 47, wherein, The symbol to be encoded indicates the position information of the vertex to be encoded. The position information is represented by N bits, where N is an integer greater than or equal to 1. Each bit in the position information divides the position interval determined by the preceding bits into two intervals, left and right. Alternatively, where N is an integer greater than or equal to 2, the first bit in the position information indicates that the position interval where the vertex is located is divided into a left interval and a right interval. The N-1 bits in the position information other than the first bit indicate that the left interval or the right interval is divided into m position intervals, where m is an integer and m is less than or equal to 2 to the power of N-1.

49. The method according to claim 47, wherein, The prediction mode for determining the symbol to be encoded includes: Based on the first syntax element and the compensation reference vertex information, a prediction mode is determined, wherein the first syntax element is configured to indicate whether inter-frame prediction is enabled, and the compensation reference vertex information is configured to indicate whether there is a compensation reference vertex for the vertex to be encoded after the inter-frame prediction mode is enabled.

50. The method according to claim 49, wherein, The process of determining the prediction mode based on the first syntax element and the uncompensated reference vertex information includes: Based on the first syntax element indicating that inter-frame prediction is not enabled or the compensation reference vertex information being configured to indicate that there is no compensation reference vertex for the vertex to be encoded after inter-frame prediction mode is enabled, the prediction mode is determined to be intra-frame prediction mode. Based on the first syntax element indicating that inter-frame prediction is enabled and the compensation reference vertex information being configured to indicate that there is a compensation reference vertex for the vertex to be encoded after inter-frame prediction mode is enabled, the prediction mode is determined to be inter-frame prediction mode.

51. The method according to claim 47, wherein, The method includes: Based on the fact that the prediction mode is an intra-frame prediction mode, the context state tree is determined to be an intra-frame context state tree.

52. The method according to claim 49, wherein, The process of determining the inter-frame context state tree of the symbol to be encoded includes: The inter-frame context state tree is determined based on the co-bit information of the compensation reference vertex information.

53. The method according to claim 52, wherein, The inter-frame context state tree includes inter-frame context state tree one and inter-frame context state tree two; the values ​​of the co-bit information of the compensation reference vertex information include 0 and 1.

54. The method according to claim 53, wherein, The determination of the inter-frame context state tree based on the same bit information of the compensation reference vertex information includes: Based on the fact that the value of the co-bit information of the compensation reference vertex information is 0, the inter-frame context is determined to be inter-frame context state tree one. Based on the fact that the value of the co-bit information of the compensation reference vertex information is 1, the inter-frame context is determined to be inter-frame context state tree two.

55. The method according to claim 48, wherein, Determining the predicted value of the symbol to be encoded includes: The predicted value is determined based on the co-occurrence bit information of the second syntax element and the uncompensated reference vertex information of the vertex to be encoded, wherein the value of the second syntax element is determined based on the prediction result of the second bit of the neighboring uncompensated reference vertex, the uncompensated reference vertex information, and the prediction result of the first bit of the uncompensated reference vertex.

56. The method according to claim 55, wherein, If the prediction result of the second bit of the neighboring uncompensated reference vertex does not meet the preset condition, or if the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist, or if the prediction result of the first bit of the uncompensated reference vertex does not meet the preset condition, the value of the second syntax element is not equal to 1. The second syntax element is set to 1 if the prediction result of the second bit of the neighboring uncompensated reference vertex meets the preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the first bit of the uncompensated reference vertex meets the preset condition.

57. The method of claim 55, wherein, Based on the fact that the value of the second syntax element is not equal to 1, the predicted value is determined to be no prediction; The predicted value is determined based on the value of the second syntax element being equal to 1, and based on the parity bit information of the uncompensated reference vertex information of the vertex to be encoded.

58. The method according to claim 57, wherein, The determination of the predicted value based on the co-bit information of the uncompensated reference vertex information of the vertex to be encoded includes: The predicted value is determined to be 0 based on the fact that the co-bit information of the uncompensated reference vertex information of the vertex to be encoded is 0. Based on the fact that the co-bit information of the uncompensated reference vertex information of the vertex to be encoded is 1, the predicted value is determined to be 1.

59. The method according to claim 58, wherein, The method further includes: The predicted value is updated based on the sixth and seventh quantity information, wherein the sixth quantity information is the cumulative sum of the second bits of the encoded vertex position information that were judged to be located, and the seventh quantity information is the cumulative sum of the second bits of the encoded vertex position information.

60. The method according to claim 59, wherein, The predicted value is updated based on whether the ratio of the sixth quantity information and the seventh quantity information is greater than a preset threshold. Based on the fact that the ratio of the sixth quantity information to the seventh quantity information is not greater than the preset threshold, the predicted value is updated to not predict. If the ratio of the sixth quantity information to the seventh quantity information is greater than the preset threshold, the predicted value will not be updated.

61. The method of claim 60, wherein the predicted value includes any one of the following: No prediction; The predicted value before the update is 0, and the predicted value after the update is no prediction. The predicted value before the update is 1, and the predicted value after the update is no prediction. The predicted value is 0; The predicted value is 1.

62. The method according to claim 61, wherein, The context probability model group includes context probability model group 1 to context probability model group 7; determining the context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 4. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0. The context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 3; Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 7.

63. The method according to claim 58, wherein the context probability model group includes context probability model group 1 to context probability model group 7; the step of determining the context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be encoded is determined to be context probability model group 7.

64. The method according to any one of claims 47-58, wherein, The inter-frame context state tree includes primary information and secondary information, with the secondary information comprising 15 bits of information.

65. The method according to any one of claims 47-63, wherein, The inter-frame context state tree includes primary information and secondary information, and the secondary information includes 16 bits of information.

66. The method according to claim 47, wherein, The determination of the context probability model based on the context probability model group corresponding to the symbol to be encoded includes: Based on the inter-frame context state tree and the context probability model group, a context probability model index is determined. The context probability model group includes one or more context probability models, and the context probability model index indicates a context probability model in the context probability model group. The context probability model indicated by the context probability model index is determined based on the inter-frame context state tree.

67. The method according to any one of claims 47-63, wherein, The symbol to be encoded is the second bit in the location information.

68. An encoder based on inter-frame prediction, comprising: The prediction mode determination module is configured to determine the prediction mode of the symbol to be encoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode. The context state tree determination module is configured to determine the inter-frame context state tree of the symbol to be encoded based on the prediction mode being an inter-frame prediction mode. The prediction module is configured to determine the predicted value of the symbol to be encoded for the inter-frame context state tree; The context probability model group is configured to determine the context probability model group corresponding to the symbol to be encoded based on the inter-frame context state tree and the predicted value, and to determine the context probability model based on the context probability model group corresponding to the symbol to be encoded, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; The entropy coding module is configured to encode the symbol to be encoded based on the context probability model.

69. The encoder according to claim 68, wherein, The symbol to be encoded is the second bit in the location information.

70. A decoding method based on inter-frame prediction, comprising: Determine the prediction mode of the symbol to be decoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode; Based on the prediction mode being an inter-frame prediction mode, the inter-frame context state tree of the symbol to be decoded is determined; For the inter-frame context state tree, determine the predicted value of the symbol to be decoded; Based on the inter-frame context state tree and the predicted value, the context probability model group corresponding to the symbol to be decoded is determined, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; Based on the context probability model group corresponding to the symbol to be decoded, determine the context probability model; The symbol to be decoded is decoded based on the context probability model.

71. The method according to claim 70, wherein, The symbol to be decoded indicates the position information of the vertex to be decoded. The position information is represented by N bits, where N is an integer greater than or equal to 1. Each bit in the position information divides the position interval determined by the preceding bits into two intervals, left and right. Alternatively, where N is an integer greater than or equal to 2, the first bit in the position information indicates that the position interval where the vertex is located is divided into a left interval and a right interval. The N-1 bits in the position information other than the first bit indicate that the left interval or the right interval is divided into m position intervals, where m is an integer and m is less than or equal to 2 to the power of N-1.

72. The method according to claim 70, wherein, The prediction mode for determining the symbol to be decoded includes: Based on the first syntax element and the compensation reference vertex information, a prediction mode is determined, wherein the first syntax element is configured to indicate whether inter-frame prediction is enabled, and the compensation reference vertex information is configured to indicate whether there is a compensation reference vertex for the vertex to be decoded after the inter-frame prediction mode is enabled.

73. The method according to claim 72, wherein, The process of determining the prediction mode based on the first syntax element and the uncompensated reference vertex information includes: Based on the first syntax element indicating that inter-frame prediction is not enabled or the compensation reference vertex information being configured to indicate that inter-frame prediction is enabled... If no compensation reference vertex for the vertex to be decoded exists after testing the prediction mode, the prediction mode is determined to be an intra-frame prediction mode. Based on the first syntax element indicating that inter-frame prediction is enabled and the compensation reference vertex information being configured to indicate that there is a compensation reference vertex for the vertex to be decoded after inter-frame prediction mode is enabled, the prediction mode is determined to be inter-frame prediction mode.

74. The method according to claim 70, wherein, The method includes: Based on the fact that the prediction mode is an intra-frame prediction mode, the context state tree is determined to be an intra-frame context state tree.

75. The method according to claim 72, wherein, The process of determining the inter-frame context state tree of the symbol to be decoded includes: The inter-frame context state tree is determined based on the co-bit information of the compensation reference vertex information.

76. The method according to claim 75, wherein, The inter-frame context state tree includes inter-frame context state tree one and inter-frame context state tree two; the values ​​of the co-bit information of the compensation reference vertex information include 0 and 1.

77. The method according to claim 76, wherein, The determination of the inter-frame context state tree based on the same bit information of the compensation reference vertex information includes: Based on the fact that the value of the co-bit information of the compensation reference vertex information is 0, the inter-frame context is determined to be inter-frame context state tree one. Based on the fact that the value of the co-bit information of the compensation reference vertex information is 1, the inter-frame context is determined to be inter-frame context state tree two.

78. The method according to claim 72, wherein, Determining the predicted value of the symbol to be decoded includes: The predicted value is determined based on the co-occurrence bit information of the second syntax element and the uncompensated reference vertex information of the vertex to be decoded, wherein the value of the second syntax element is determined based on the prediction result of the second bit of the neighboring uncompensated reference vertex, the uncompensated reference vertex information, and the prediction result of the first bit of the uncompensated reference vertex.

79. The method according to claim 78, wherein, If the prediction result of the second bit of the neighboring uncompensated reference vertex does not meet the preset condition, or if the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist, or if the prediction result of the first bit of the uncompensated reference vertex does not meet the preset condition, the value of the second syntax element is not equal to 1. The second syntax element is set to 1 if the prediction result of the second bit of the neighboring uncompensated reference vertex meets the preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the first bit of the uncompensated reference vertex meets the preset condition.

80. The method according to claim 78, wherein, Based on the fact that the value of the second syntax element is not equal to 1, the predicted value is determined to be no prediction; The predicted value is determined based on the value of the second syntax element being equal to 1, and based on the parity bit information of the uncompensated reference vertex information of the vertex to be decoded.

81. The method according to claim 80, wherein, The determination of the predicted value based on the same-bit information of the uncompensated reference vertex information of the vertex to be decoded includes: The predicted value is determined to be 0 based on the fact that the co-bit information of the uncompensated reference vertex information of the vertex to be decoded is 0. Based on the fact that the co-bit information of the uncompensated reference vertex information of the vertex to be decoded is 1, the predicted value is determined to be 1.

82. The method according to claim 81, wherein, The method further includes: The predicted value is updated based on the sixth and seventh quantity information, wherein the sixth quantity information is the cumulative sum of the second bits of the decoded vertex position information that were judged to be located, and the seventh quantity information is the cumulative sum of the second bits of the decoded vertex position information.

83. The method according to claim 82, wherein, The predicted value is updated based on whether the ratio of the sixth quantity information and the seventh quantity information is greater than a preset threshold. Based on the fact that the ratio of the sixth quantity information to the seventh quantity information is not greater than the preset threshold, the predicted value is updated to not predict. If the ratio of the sixth quantity information to the seventh quantity information is greater than the preset threshold, the predicted value will not be updated.

84. The method of claim 83, wherein the predicted value includes any one of the following: No prediction; The predicted value before the update is 0, and the predicted value after the update is no prediction. The predicted value before the update is 1, and the predicted value after the update is no prediction. The predicted value is 0; The predicted value is 1.

85. The method according to claim 84, wherein, The context probability model group includes context probability model group 1 to context probability model group 7; determining the context probability model group corresponding to the symbol to be decoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 4. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 0 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, the prediction value being 1 before the update and no prediction after the update, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 7. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0. The context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 6; Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 7.

86. The method according to claim 81, wherein the context probability model group includes context probability model group 1 to context probability model group 7; the step of determining the context probability model group corresponding to the symbol to be decoded based on the inter-frame context state tree and the predicted value includes: Based on the prediction mode being intra-frame prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 1. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group two. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group three. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree one, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group four. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being no prediction, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 5. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 0, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 6. Based on the prediction mode being inter-frame prediction, the inter-frame context state tree being inter-frame context state tree two, and the prediction value being 1, the context probability model group corresponding to the symbol to be decoded is determined to be context probability model group 7.

87. The method according to any one of claims 70-81, wherein, The inter-frame context state tree includes primary information and secondary information, with the secondary information comprising 15 bits of information.

88. The method according to any one of claims 70-86, wherein, The inter-frame context state tree includes primary information and secondary information, and the secondary information includes 16 bits of information.

89. The method according to claims 70-86, wherein, The determination of the context probability model based on the context probability model group corresponding to the symbol to be decoded includes: Based on the inter-frame context state tree and the context probability model group, a context probability model index is determined. The context probability model group includes one or more context probability models, and the context probability model index indicates a context probability model in the context probability model group. The context probability model indicated by the context probability model index is determined based on the inter-frame context state tree.

90. The method according to any one of claims 70-86, wherein, The symbol to be decoded is the second bit in the location information.

91. A decoder based on inter-frame prediction, comprising: The prediction mode determination module is configured to determine the prediction mode of the symbol to be decoded, wherein the prediction mode includes intra-frame prediction mode and inter-frame prediction mode. The context state tree determination module is configured to determine the inter-frame context state tree of the symbol to be decoded based on the prediction mode being an inter-frame prediction mode. The prediction module is configured to determine the predicted value of the symbol to be decoded for the inter-frame context state tree; The context probability model group is configured to determine the corresponding symbol to be decoded based on the inter-frame context state tree and the predicted value. The context probability model is determined by the context probability model group and the context probability model group corresponding to the symbol to be decoded, wherein different inter-frame context state trees and different predicted values ​​correspond to different context probability model groups; The entropy decoding module is configured to decode the symbol to be decoded based on the context probability model.

92. The decoder according to claim 91, wherein, The symbol to be decoded is the second bit in the location information.

93. An apparatus comprising: The memory is configured to store computer-readable instructions; The processor is configured to invoke the computer-readable instructions to perform the method as described in any one of claims 1-21, 24-44, 47-67, and 70-90.

94. A non-volatile computer storage medium storing computer-readable instructions that, when invoked by a processor, cause the processor to perform the method as described in any one of claims 1-21, 24-44, 47-67, and 70-90.

95. A chip, comprising: The memory is configured to store computer-readable instructions; The processor is configured to invoke the computer-readable instructions to perform the method as described in any one of claims 1-21, 24-44, 47-67, and 70-90.

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