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

Interframe prediction-based coding method and device, chip and storage medium

[0001] The present 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 contents of all of which are incorporated herein by reference in their entirety. TECHNICAL FIELD

[0002] The present application relates to the field of audio and video technology, and in particular to an interframe prediction-based coding method and device, chip and storage medium. BACKGROUND

[0003] A point cloud is a three-dimensional data set composed of a large number of points, each point containing position information and possibly other attributes (such as color, normal vector, texture coordinate and intensity value, etc.) in space. Point cloud data includes rich spatial information and attribute information, so it can express three-dimensional media content and can be configured to provide VR (Virtual Reality), AR (Virtual Reality), MR (Mixed Reality) and other services. However, point cloud data often includes tens of thousands or even hundreds of thousands of point data to represent, and the amount of data is large, so a method for effectively processing point cloud data is needed to compress the point cloud data.

[0004] SUMMARY

[0005] To solve the problem of large amount of point cloud data, according to some embodiments provided by the present application, the compression of point cloud data is realized to reduce the amount of data.

[0006] Some embodiments of the present application provide an inter prediction based encoding method. The method comprises: determining a prediction mode of a to-be-encoded symbol, wherein the prediction mode comprises an intra prediction mode and an inter prediction mode; determining an inter context state tree of the to-be-encoded symbol based on the prediction mode being the inter prediction mode, wherein the inter context state tree of the to-be-encoded symbol is an independent OBUF inter context state tree; determining a prediction value of the to-be-encoded symbol for the inter context state tree; determining a context probability model group corresponding to the to-be-encoded symbol based on the inter context state tree and the prediction value, wherein different inter context state trees and different prediction values correspond to different context probability model groups; determining a context probability model based on the context probability model group corresponding to the to-be-encoded symbol; and encoding the to-be-encoded symbol based on the context probability model.

[0007] According to some embodiments of the present application, a context probability model group suitable for each state is selected for a context state tree according to an inter context state tree and a prediction value of a to-be-encoded symbol, thereby more fully utilizing inter prediction information, improving prediction effect, and reducing the bit number of the context state tree and saving memory without affecting encoding efficiency.

[0008] Some embodiments of the present application provide an inter prediction based encoder. The encoder comprises: a prediction mode determination module configured to determine a prediction mode of a to-be-encoded symbol, wherein the prediction mode comprises an intra prediction mode and an inter prediction mode; a context state tree determination module configured to determine an inter context state tree of the to-be-encoded symbol based on the prediction mode being the inter prediction mode, wherein the inter context state tree of the to-be-encoded symbol is an independent OBUF inter context state tree; a prediction module configured to determine a prediction value of the to-be-encoded symbol for the inter context state tree; a context probability model group configured to determine a context probability model group corresponding to the to-be-encoded symbol based on the inter context state tree and the prediction value, and determine a context probability model based on the context probability model group corresponding to the to-be-encoded symbol, wherein different inter context state trees and different prediction values correspond to different context probability model groups; and an entropy encoding module configured to encode the to-be-encoded symbol based on the context probability model.

[0009] Some embodiments of the present application provide a decoding method based on inter prediction. The method comprises: determining a prediction mode of a to-be-decoded symbol, wherein the prediction mode comprises an intra prediction mode and an inter prediction mode; determining an inter context state tree of the to-be-decoded symbol based on the prediction mode being the inter prediction mode; determining a prediction value of the to-be-decoded symbol according to the inter context state tree; determining a context probability model group corresponding to the to-be-decoded symbol based on the inter context state tree and the prediction value, wherein different inter context state trees and different prediction values correspond to different context probability model groups; determining a context probability model based on the context probability model group corresponding to the to-be-decoded symbol; and decoding the to-be-decoded symbol based on the context probability model.

[0010] Some embodiments of the present application provide a decoder based on inter prediction. The decoder comprises: a prediction mode determination module configured to determine a prediction mode of a to-be-decoded symbol, wherein the prediction mode comprises an intra prediction mode and an inter prediction mode; a context state tree determination module configured to determine an inter context state tree of the to-be-decoded symbol based on the prediction mode being the inter prediction mode; a prediction module configured to determine a prediction value of the to-be-decoded symbol for the inter context state tree; a context probability model group configured to determine a context probability model group corresponding to the to-be-decoded symbol based on the inter context state tree and the prediction value, and determine a context probability model based on the context probability model group corresponding to the to-be-decoded symbol, wherein different inter context state trees and different prediction values correspond to different context probability model groups; and an entropy decoding module configured to decode the to-be-decoded symbol based on the context probability model.

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

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

[0013] Some embodiments of the present application provide a chip. The chip comprises: a memory configured to store computer readable instructions; and a processor configured to invoke the computer readable instructions to perform the method as described above. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

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

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

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

[0018] FIG. 4 shows a schematic diagram of Trisoup-based geometry reconstruction, according to some embodiments;

[0019] FIG. 5 shows an exemplary block diagram of an encoder using OBUF technique for encoding, according to some embodiments;

[0020] FIG. 6 shows an exemplary diagram of neighbor vertices of an edge to be encoded located on x, y, z axes, according to some embodiments;

[0021] FIG. 7 shows an exemplary block diagram of an encoding module, according to some embodiments;

[0022] FIG. 8 shows an exemplary diagram of information structure of secondary information in an inter-frame context state tree, according to some embodiments;

[0023] FIG. 9 shows an exemplary diagram of information structure of secondary information in an inter-frame context state tree, according to some embodiments;

[0024] FIG. 10 shows an exemplary diagram of information structure of secondary information in an inter-frame context state tree, according to some embodiments;

[0025] FIG. 11 shows an exemplary block diagram of a decoding module, according to some embodiments;

[0026] FIG. 12 shows an exemplary flow of an encoding process using OBUF technique, according to some embodiments;

[0027] FIG. 13 shows an exemplary flow of an encoding process of Trisoup geometry encoding based on inter-frame prediction, according to some embodiments;

[0028] FIG. 14 shows an exemplary flow of a decoding process of Trisoup geometry decoding based on inter-frame prediction, according to some embodiments;

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

[0030] FIG. 16 shows an exemplary flowchart of an encoding process of Trisoup geometry coding based on inter prediction according to some embodiments;

[0031] FIG. 17 shows an exemplary flowchart of a decoding process of Trisoup geometry coding based on inter prediction according to some embodiments. DETAILED DESCRIPTION

[0032] In some embodiments, the method provided by some embodiments of the present application can implement the encoding of Trisoup vertices in G-PCC. More specifically, the method provided by some embodiments of the present application can implement the encoding of position information in Trisoup vertex information in G-PCC.

[0033] In some embodiments, the position information includes N bits, a first bit in the N bits indicates that the position interval in which the Trisoup vertex is located 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 position interval in which it is located, 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 position interval in which it is located. 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 position interval in which it is located, 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 position interval in which it is located. The other N-1 bits in the N bits, excluding the first bit, indicate that the left interval or the right interval in which the Trisoup vertex is located is divided into m parts to indicate the quantized distance of the Trisoup vertex to the midpoint of the position interval in which it is located. In some embodiments, the left interval or the right interval in which the Trisoup vertex is located is divided into m equal parts, and the value of m is determined according to the value of N. In some embodiments, the value of m is 2 raised to the power of N-1. In some embodiments, the value of m can also be less than 2 raised to the power of N-1. In some embodiments, the left interval or the right interval in which the Trisoup vertex is located can be divided into m equal parts. In some embodiments, the left interval or the right interval in which the Trisoup vertex is located can be further divided into m unequal parts.

[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 2 position intervals, one of which is a position interval relatively close to the quantization distance to the midpoint of the position interval where the Trisoup vertex is located, which can be recorded as the first position interval in the left interval, and the other is a position interval relatively far from the quantization distance to the midpoint of the position interval where the Trisoup vertex is located, which can be recorded 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 2 position intervals, one of which is a position interval relatively close to the quantization distance to the midpoint of the position interval where the Trisoup vertex is located, which can be recorded as the first position interval in the right interval, and the other is a position interval relatively far from the quantization distance to the midpoint of the position interval where the Trisoup vertex is located, which can be recorded 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 position interval relatively close to the quantization distance to the midpoint of the position interval where the Trisoup vertex is located in the left interval, and 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 position interval relatively far from the quantization distance to the midpoint of the position interval where the Trisoup vertex is located in the left 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 the position interval relatively close to the quantization distance to the midpoint of the position interval where the Trisoup vertex is located in the left interval, and 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 position interval relatively far from the quantization distance to the midpoint of the position interval where the Trisoup vertex is located in the left interval.

[0035] For example, when the 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 bit and the third bit in the position information indicate that the left interval is further divided into 4 position intervals, and the quantization distances from the midpoint of the position interval where the Trisoup vertex is located to the 4 position intervals are recorded in order of proximity to distance as position interval 1, position interval 2, position interval 3, and position interval 4. For example, when the values of the second bit and the third bit are 00, it indicates that the Trisoup vertex is located in the position interval closest to the midpoint of the position interval where the Trisoup vertex is located, i.e., position interval 1; when the values of the second bit and the third bit are 01, it indicates that the Trisoup vertex is located in the relatively close position interval to the midpoint of the position interval where the Trisoup vertex is located, i.e., position interval 2; when the values of the second bit and the third bit are 10, it indicates that the Trisoup vertex is located in the relatively far position interval to the midpoint of the position interval where the Trisoup vertex is located, i.e., position interval 3; and when the values of the second bit and the third bit are 11, it indicates that the Trisoup vertex is located in the position interval farthest to the midpoint of the position interval where the Trisoup vertex is located, i.e., position interval 4. Of course, it can be understood that when the values of the second bit and the third bit are 11, it indicates that the Trisoup vertex is located in the position interval closest to the midpoint of the position interval where the Trisoup vertex is located, i.e., position interval 1. That is, the embodiments of the present application do not limit which value corresponds to which position interval.

[0036] For example, when the 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 bit, the third bit, and the fourth bit in the position information indicate that the left interval is further divided into 8 position intervals. Which value of the second bit, the third bit, and the fourth bit corresponds to which position interval in the 8 position intervals is consistent with the concept in the above example, and will not be repeated here.

[0037] In some embodiments, the position information includes N bits, each bit indicating to divide the position interval where the Trisoup vertex currently locates into left and right two intervals, and the value of the bit indicates whether the interval where the Trisoup vertex locates is the left interval or the right interval. Wherein, the position interval where the Trisoup vertex currently locates is the position interval determined based on the previous bits. In some embodiments, when the value of the bit is 0, the value of the bit indicates that the Trisoup vertex is located in the left interval of the position interval where it currently locates, and when the value of the bit is 1, the value of the bit indicates that the Trisoup vertex is located in the right interval of the position interval where it currently locates. In some embodiments, when the value of the bit is 0, the value of the bit indicates that the Trisoup vertex is located in the right interval of the position interval where it currently locates, and when the value of the bit is 1, the value of the bit indicates that the Trisoup vertex is located in the left interval of the position interval where it currently locates.

[0038] In some embodiments, the N bits in the position information indicate that the position interval where the Trisoup vertex locates is divided N times, wherein the first bit in the N bits indicates to divide the position interval where the Trisoup vertex locates into left and right two intervals, and the value of the first bit determines whether the Trisoup vertex is located in the left interval or the right interval. The second bit in the N bits indicates to continue to divide the interval where the Trisoup vertex locates into left and right two intervals, and the value of the second bit determines whether the Trisoup vertex is located in the left interval or the right interval of the interval where it locates. Similarly, the subsequent bits in the N bits are also in the above manner, and after being divided N times, the position of the Trisoup vertex is finally determined.

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

[0040] In some embodiments, the position information of the Trisoup vertex includes N bits, and the N bits indicate which interval of the edge the Trisoup vertex currently locates in. For example, when N equals 2 and the edge is divided into 3 intervals, if the 2 bits are 00, it means the Trisoup vertex currently locates in the first interval of the edge; if the 2 bits are 01, it means the Trisoup vertex currently locates in the second interval of the edge; if the 2 bits are 10, it means the Trisoup vertex currently locates in the third interval of the edge. For another example, when N equals 2 and the edge is divided into 4 intervals, the 2 bits can be 00, 01, 10, 11, which respectively represent that the Trisoup vertex currently locates in the first interval, the second interval, the third interval and the fourth interval of the edge.

[0041] In some embodiments, the position information of the Trisoup vertex includes N bits, and the N bits indicate which interval of the edge the Trisoup vertex currently locates in. For example, when N equals 2 and the edge is divided into 3 intervals, if the 2 bits are 00, it means the Trisoup vertex currently locates in the first interval of the edge; if the 2 bits are 01, it means the Trisoup vertex currently locates in the second interval of the edge; if the 2 bits are 10, it means the Trisoup vertex currently locates in the third interval of the edge. For another example, when N equals 2 and the edge is divided into 4 intervals, the 2 bits can be 00, 01, 10, 11, which respectively represent that the Trisoup vertex currently locates in the first interval, the second interval, the third interval and the fourth interval of the edge.

[0042] In some embodiments, the method provided by some embodiments of the present application can encode the first bit of the position information in the Trisoup vertex. In some embodiments, the method provided by some embodiments of the present application comprises: determining a prediction mode of a to-be-encoded symbol, wherein the prediction mode comprises an intra prediction mode and an inter prediction mode; determining an inter context state tree of the to-be-encoded symbol based on the prediction mode being the inter prediction mode, wherein the inter context state tree of the to-be-encoded symbol is an independent OBUF inter context state tree; determining a prediction value of the to-be-encoded symbol for the inter context state tree; determining a context probability model group corresponding to the to-be-encoded symbol based on the inter context state tree and the prediction value, wherein different inter context state trees and different prediction values correspond to different context probability model groups; determining a context probability model based on the context probability model group corresponding to the to-be-encoded symbol; and encoding the to-be-encoded symbol based on the context probability model. According to the method of the present application, some embodiments of the present application select a context probability model group suitable for each state for the context state tree according to the inter prediction result, thereby more fully utilizing the inter prediction information, and reducing the number of bits of the context state tree and saving memory without affecting the Solid G-PCC set encoding efficiency.

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

[0044] In some embodiments, the method provided by some embodiments of the present application further comprises: determining, based on the fourth quantity information and the fifth quantity information, whether the inter prediction information is predicted as 0 or 1, so as to update part of the inter prediction information from being predicted as 0 or 1 to not being predicted. The fourth quantity information is the cumulative sum of the first bits in the position information of the coded vertex that are determined as being predicted (including being predicted as 0 or 1), and the fifth quantity information is the cumulative sum of the first bits in the position information of the coded vertex. In some embodiments, the above operation can merge part of the inter context state tree, so as to avoid slow convergence of the probability corresponding to the inter context state tree and poor prediction effect when the use of inter prediction is less.

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

[0046] In some embodiments, the context probability model group can comprise 13 context probability model groups. Each inter context state tree corresponds to three prediction values, i.e. not predicted, predicted as 0, and predicted as 1. In some embodiments, each context probability model group corresponds to an inter context state tree and a prediction value. For example, in some embodiments, one of the context probability model groups in the context probability model group can be configured to determine the context probability model based on the intra context state tree. In some embodiments, the remaining 12 context probability model groups in the context probability model group can be configured to determine the context probability model based on the inter context state tree one, the inter context state tree two, the inter context state tree three, the inter context state tree four, and the corresponding prediction value. In some embodiments, after determining the context state tree of the first bit in the position information of the vertex to be coded, the method provided by some embodiments of the present application maps the inter context state tree to the corresponding context probability model group according to the prediction value to determine the context probability model for subsequent coding when performing inter prediction.

[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 a corresponding context probability model index based on the determined context state tree, where the context probability model index can 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 to-be-encoded symbol can be encoded based on the context probability model.

[0049] In some embodiments, the inter-prediction-based encoding apparatus, storage medium and method provided in the present application are based on the same concept, and the implementation of the apparatus and the storage medium can be referred to each other because the principles of solving technical problems are similar, and the repeated parts will not be described again.

[0050] In some embodiments, the inter-prediction-based decoding apparatus, storage medium and method provided in the present application are based on the same concept as the inter-prediction-based encoding apparatus, storage medium and method provided in the present application, and the principles of the technology adopted are similar, so 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 obtaining the three-dimensional space coordinates of each sampling point on the surface of an object, a collection of points is obtained, which is called a point cloud. The points in the point cloud have not only geometric information such as geometric coordinates, but also some other attribute information such as color, normal vector, reflectivity, transparency, material type, etc. The point cloud can be obtained in various ways. In some embodiments, the implementation of obtaining the point cloud includes using a camera array with known fixed positions in space to observe an object, and using some related algorithms to obtain the three-dimensional representation of the object from the two-dimensional images taken by the camera array, thereby obtaining the point cloud corresponding to the object. In other embodiments, the implementation of obtaining the point cloud includes using a laser radar scanning device to obtain the point cloud corresponding to the object. The sensor of the laser radar scanning device records the electromagnetic waves reflected by the object surface after being emitted by the radar, thereby obtaining the volume information of the object, and obtaining the point cloud corresponding to the object according to the volume information of the object. In other embodiments, the implementation of obtaining the point cloud can also include creating three-dimensional volume information from two-dimensional images by using artificial intelligence or computer vision algorithms, thereby obtaining the point cloud corresponding to the object.

[0052] Point cloud provides a high-precision three-dimensional representation for fine digitalization of the physical world, and can be widely applied in fields such as three-dimensional modeling, smart city, autonomous navigation system, augmented reality, etc. However, due to the characteristics of massive data, unstructured data, and uneven density, the storage and transmission of point cloud face great challenges. Therefore, it is necessary to efficiently compress the point cloud. At present, the compression standards for point cloud include Geometry-based Point Cloud Compression (G-PCC) and Video-based Point Cloud Compression (V-PCC). The principles and related algorithms of G-PCC are described below.

[0053] FIG. 1 shows an exemplary block diagram of a system 100 that can be configured for G-PCC encoding and decoding, according to some embodiments of the present application. As shown in FIG. 1, the system 100 can be configured for encoding and / or decoding point cloud data, i.e., supporting point cloud compression and / or decompression. The system can efficiently 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 can include a source device 110. The source device 110 can be configured to provide encoded point cloud data to the target device 120 for decoding.

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

[0056] In some embodiments, the system 100 can include a storage device 130. The source device 110 can store the encoded point cloud data in the storage device 130 after encoding the point cloud data. 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 a computer, a notebook, a Pad, a set-top box, a television, a camera, a display device, a digital media player, a video gaming console, a video streaming device, a land or sea vehicle, a spacecraft, an airplane, a robot, a LIDAR (Light Detection and Ranging) device, a satellite, and the like. In some embodiments, the source device 110 and the target device 120 can also employ wireless communication and can also employ wired communication. In some embodiments, the storage device 130 can be any suitable storage device and / or storage medium, such as a file server, a cloud server, a communication medium, a storage device, and the like.

[0058] In some embodiments, the source device 110 can include a data source 111. The data source 111 can be configured to obtain point cloud data, in particular, point cloud data that has not been encoded. The data source 111 can be configured to provide a series of consecutive data frames to the G-PCC encoder 113 for encoding. The data source 111 can include a point cloud data obtaining device, which can be any one or more of a camera or a sensor, such as a 3D scanner, a LIDAR sensor, a video camera, and the like. In some embodiments, the point cloud data obtaining device can also obtain historical point cloud data that has been obtained but not yet encoded, or corresponding data from a data content provider. In summary, the point cloud data obtaining device can be configured to obtain point cloud data that can be configured to be encoded, and the point cloud data obtaining device can obtain the point cloud data in any suitable manner.

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

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

[0061] In some embodiments, the source device 110 can include an output interface 114. The output interface 114 can 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 can 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 can be configured to transmit encoded data according to a cellular communication standard, e.g., 4G, 4G-LTE, 5G, etc. In some embodiments, the output interface 114 can also be configured to transmit encoded data according to other wireless standards, e.g., IEEE 802.11 specifications, IEEE 802.15 specifications (e.g., ZigBee TM ), Bluetooth TM standards, etc.

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

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

[0064] In some embodiments, the target device 120 can include a second memory 122. The second memory 122 can be configured to store the decoded point cloud data. In some embodiments, the second memory 122 and the first memory 112 can be memories in different devices. In some embodiments, the second memory 122 and the first memory 112 can also be different memories or the same memory 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 storing instructions.

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

[0066] In some embodiments, the target device 120 can include an input interface 124; wherein the input interface 124 can receive and / or obtain one or more bitstreams from the source device 110 and / or the 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 can 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 can be configured to transmit encoded data according to a cellular communication standard, e.g., 4G, 4G-LTE, 5G, etc. In some embodiments, the input data 124 can also be configured to transmit encoded data according to other wireless standards, e.g., IEEE 802.11 specifications, IEEE 802.15 specifications (e.g., ZigBee TM ), Bluetooth TM standards, etc.

[0067] In some embodiments, the target device 120 can include any suitable components that can be configured to decode point cloud data.

[0068] In some embodiments, the G-PCC encoder 113 can be any of a variety of encoding and decoding circuitry, such as a microprocessor, a digital signal processor, an image processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), discrete logic, software, hardware, firmware or any combinations thereof. In some embodiments, when the encoding and / or decoding techniques are implemented in software, the source device 110 and the destination device 120 can store instructions for the software in a suitable, non- volatile storage medium and execute the instructions using one or more processors described previously. The G-PCC encoder 113 and the G-PCC decoder 123 can be included in one or more encoders and / or decoders, a one or more encoders can be integrated as part of a combined encoder / decoder (CODEC) in a device, a one or more decoders can be integrated as part of a combined encoder / decoder (CODEC) in a device, an encoder and a decoder can be integrated as part of a combined encoder / decoder (CODEC) in a device. In some embodiments, a device including the G-PCC encoder 113 and the G-PCC decoder 123 can include at least one or more integrated circuits, microprocessors.

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

[0070] FIG. 2 illustrates an example block diagram of a G-PCC encoder 200, according to some embodiments of the present disclosure. In some embodiments, the G-PCC encoder 200 can be the G-PCC encoder 113 of FIG. 1, a device including the G-PCC encoder 113 of FIG. 1. As shown in FIG. 2, the G-PCC encoder 200 can include any suitable means for encoding geometry information. In some embodiments, the attribute encoding module 220 can include any suitable means for encoding attribute information.

[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 geometry information and attribute information.

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

[0073] In some embodiments, the geometry encoding module 210 can include a coordinate conversion unit 211. The coordinate conversion unit 211 can be configured to convert the coordinates of the geometry information, to obtain geometry-converted geometry information. The geometry-converted geometry information can be such that all the to-be-encoded point clouds are contained in a bounding box. In some embodiments, the bounding box can be the smallest cube that can completely enclose all the to-be-encoded point clouds, and the origin coordinates of the bounding box can be the minimum values of the to-be-encoded point clouds in the x, y, and z axes. The geometry information obtained after coordinate conversion can be determined according to the positions of the to-be-encoded point clouds relative to the origin coordinates.

[0074] In some embodiments, the geometry encoding module 210 can include a voxelization unit 212. The voxelization unit 212 can be configured to voxelize the geometry information converted in coordinates. In some embodiments, the voxelization unit 212 voxelizing the geometry information converted in coordinates means that the voxelization unit 212 quantizes and removes duplicate points of the geometry information converted in coordinates, thereby scaling the to-be-encoded point clouds. Since quantization rounding can make the positions of some points in the to-be-encoded point clouds the same, the voxelization unit 212 needs to determine whether to remove duplicate points according to parameters.

[0075] In some embodiments, the geometry encoding module 210 can include an octree encoding unit 213. The octree encoding unit 213 can be configured to arithmetically encode the geometry information after voxelization, to obtain a geometry information bitstream of the to-be-encoded point clouds.

[0076] In some embodiments, the geometry encoding module 210 can include a prediction tree encoding unit 214. The prediction tree encoding unit 214 can be configured to arithmetically encode the geometry information after voxelization, to obtain a geometry information bitstream of the to-be-encoded point clouds.

[0077] In some embodiments, the geometry encoding module 210 can include a first arithmetic encoding unit 215. In some embodiments, the G-PCC encoder 200 can arithmetically encode the geometry information after voxelization through the octree encoding unit 213 and the first arithmetic encoding unit 215. In some embodiments, the G-PCC encoder 200 can arithmetically encode the geometry information after voxelization through the prediction tree encoding unit 214 and the first arithmetic encoding unit 215.

[0078] In some embodiments, the octree encoding unit 213 comprises an octree partition subunit 2131. The octree partition subunit 2131 can be configured to perform octree partitioning. In some embodiments, the octree partition subunit 2131 can be configured to perform octree partitioning on the bounding box in a breadth first search order, and obtain the occupancy code of the nodes of the octree according to the result of each partitioning. That is, the octree partition subunit 2131 can be configured to successively partition the bounding box to obtain subcubes, and continue to partition the non-empty (containing points in the point cloud) subcubes until the leaf nodes obtained by the partitioning are 1x1x1 unit cubes. In an embodiment, the octree partition subunit 2131 can be configured to encode the occupancy code generated during octree partitioning and the number of points contained in the leaf nodes, thereby completing the octree-based geometry encoding and generating a binary code stream. According to whether there are points occupying the eight subcubes obtained by each octree partitioning, the occupancy information of the eight subcubes obtained by the octree partitioning is generated, i.e. 8-bit binary data. The generated 8-bit binary code is referred to as occupancy code. In some embodiments, when the subcube contains points, the value of the corresponding bit is 1; when the subcube does not contain points, the value of the corresponding bit is 0.

[0079] In some embodiments, the octree encoding unit 213 can comprise a surface fitting subunit 2132 and any suitable unit that can be configured to perform octree-based geometry encoding. In some embodiments, the surface fitting subunit 2132 can be configured to perform surface fitting on the geometry information based on Trisoup (Triangle soup) to obtain the surface formed by the distribution of the point cloud in each subblock. The surface fitting subunit 2132 can perform octree partitioning when performing surface fitting, but does not need to partition the point cloud to be encoded to a unit cube with an edge length of 1x1x1 at each level, but rather stops partitioning when the edge length of the subblock is a preset value, and then obtains at most twelve vertices generated by the surface formed by the distribution of the point cloud in each subblock and the twelve edges of the subblock, for example, the preset value can be 32, 16 or 9. The first arithmetic encoding unit 215 can be configured to successively encode the vertex coordinates on the edges of each subblock to generate a binary geometry information code stream.

[0080] In some embodiments, the prediction tree encoding 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 points in the point cloud to be encoded and construct a prediction tree structure in two different ways (e.g., a high latency slow way and a low latency fast way), where the sorting can be in disorder, in Morton order, in azimuth angle order, in radial distance order, etc. The prediction tree encoding unit 214 can include a prediction quantization subunit 2142 and other suitable units that can be configured to perform prediction tree based geometry coding. The prediction quantization subunit 2142 can traverse each node in the prediction tree structure based on the prediction tree structure, predict several position information of the node by selecting different prediction modes to obtain prediction residuals, and quantize the prediction residuals using a quantization parameter to obtain residual quantization values to reduce data volume. The prediction modes can include at least an intra prediction mode and an inter prediction mode. The first arithmetic encoding unit 215 can iteratively perform arithmetic encoding on the prediction residuals of the position information of the nodes of the prediction tree structure, the prediction tree structure, and the quantization parameter, etc., to further generate a binary geometry information bitstream.

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

[0082] In some embodiments, the attribute encoding module 220 can be configured to encode the attribute information in a region adaptive hierarchical transform coding (RAHT) manner, an interpolation-based hierarchical nearest-neighbor prediction manner, or an interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step manner. In some embodiments, the attribute encoding module 220 can include a color conversion unit 221. The color conversion unit 221 can be configured to perform color conversion on the attribute information to convert RGB components of a pixel point to YUV components.

[0083] In some embodiments, the attribute encoding module 220 can include a re-coloring unit 222. The re-coloring unit 222 can be configured to re-color the geometry information to obtain attribute values of the reconstructed point cloud. The re-coloring 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, so as to minimize the error.

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

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

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

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

[0088] FIG. 3 is an exemplary block diagram of a G-PCC decoder 300 according to some embodiments of the present disclosure. In some embodiments, the G-PCC decoder 300 can be the G-PCC decoder 123 of FIG. 1. As shown in FIG. 3, the G-PCC decoder 300 can include any suitable units that can be configured to decode geometry information. In some embodiments, the attribute decoding module 320 can include any suitable units that can be configured to decode attribute information.

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

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

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

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

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

[0094] In some embodiments, the geometry decoding module can include an inverse coordinate conversion unit 315. The inverse coordinate conversion unit 315 can be configured to perform inverse coordinate conversion on the reconstructed geometry information to output the geometry information.

[0095] In some embodiments, the octree decoding unit 312 can include an octree synthesis subunit 3121. The octree synthesis subunit 3121 can be configured to synthesize an octree. In some embodiments, the octree decoding unit 312 can include a surface fitting subunit 3122. The surface fitting subunit 3122 can be configured to perform surface fitting to reconstruct the geometry information. In some embodiments, the octree decoding unit 312 can parse the occupancy codes of each node in a breadth-first traversal order, and successively divide the nodes until a 1x1x1 unit cube is obtained, parse the number of points contained in each leaf node, and finally recover the geometry reconstruction point cloud information.

[0096] In some embodiments, the prediction tree decoding unit 313 can comprise a prediction tree reconstruction subunit 3131. The prediction tree reconstruction subunit 3131 can be configured to reconstruct the prediction tree. In some embodiments, the prediction tree decoding unit 313 can comprise a residual calculation subunit 3132. The residual calculation subunit 3132 can be configured to obtain the geometric position prediction residual information and the quantization parameter of each prediction node and perform inverse quantization, so as to be configured to the geometry reconstruction unit 314 and the inverse coordinate conversion unit 315 to obtain the geometry information.

[0097] In some embodiments, the G-PCC decoder 300 can comprise an attribute decoding module 320. The attribute decoding module 320 can be configured to decode the attribute information code stream to obtain the attribute information.

[0098] In some embodiments, the attribute decoding module 320 can comprise a second arithmetic decoding unit 331. The second arithmetic decoding unit 331 can be configured to perform arithmetic decoding on the attribute information code stream.

[0099] In some embodiments, the attribute decoding module 320 can comprise an inverse quantization unit 332. The inverse quantization unit 332 can be configured to perform inverse quantization on the attribute information code stream after arithmetic decoding.

[0100] In some embodiments, the attribute decoding module 320 can comprise a RAHT transformation unit 333. The RAHT transformation unit 333 can be configured to perform inverse RAHT transformation on the attribute information code stream.

[0101] In some embodiments, the attribute decoding module 320 can comprise a color inverse transformation unit 334. The color inverse transformation unit 334 can be configured to perform color inverse transformation on the attribute information code stream after inverse transformation to obtain the attribute information.

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

[0103] In the G-PCC encoder as illustrated in FIG. 2 and the G-PCC decoder as illustrated in FIG. 3, the octree encoding unit 213 and the octree decoding unit 312 can both perform Trisoup-based geometry encoding and geometry decoding. In performing Trisoup-based geometry encoding, the octree encoding unit 213 can first perform octree partitioning to divide the point cloud to be encoded into leaf nodes of a specified edge length; then, surface information formed by voxels in a node is represented by a series of triangle meshes, and the series of triangle meshes form a triangle patch. In some embodiments, G-PCC generally uses a parameter trisoup node size to represent the size of a block in which a triangle patch is located. When the size of the block in which the triangle patch is located is greater than 0, a set of voxels in a node can be represented by a geometry patch, and vertices of at most twelve intersection points generated by twelve edges of the block in which the triangle patch is located are referred to as vertices.

[0104] In some embodiments, the presence of the vertices on the edges of the block represented by each node and the quantized positions of the vertices on the edges can be represented as 1 bit and N bits, respectively, and the vertex-related information of each block in which a triangle patch is located is sequentially encoded to generate a binary code stream. In some embodiments, the N can be greater than or equal to 1, for example, can be equal to 2. Inside each node, the reconstructed surface is constructed by a non-planar polygon formed by the vertices and organized into a set of triangles. In addition, each node also has a centroid vertex, and encoding an offset value (along the surface normal n) between the final centroid coordinate and the initial centroid coordinate can improve the representation of the surface curvature in each node. In addition, surface vertices can also be created and marked. FIG. 4 provides a schematic diagram of Trisoup-based geometry reconstruction in some embodiments of the present application. In some embodiments, the octree encoding unit can use a fiber tracking technique to convert triangle voxels into points to reconstruct a point cloud.

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

[0106] In performing Trisoup-based geometry decoding, the octree decoding unit 312 can employ a triangle rasterization technique to decode geometry coordinates of the point cloud from the triangle patches of the nodes. For example, the octree decoding unit 312 checks whether each voxel within a node cube intersects with the triangle patches, and if a voxel in the node cube intersects with a triangle patch, the intersection point is calculated and the decoded cube is output. In some embodiments, the octree decoding unit 312 can use 6 unit vectors (0, 1, 1), (0, 1, 1), (0, 1, 1), (0, 1, 1), (0, 1, 1), (0, 1, 1) for intersection testing to determine whether each unit vector intersects with the triangle patch. 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 position information of the vertex when the vertex exists, wherein the information indicating whether the vertex exists can be identified by one 1-bit, and the position information can be represented by N bits, wherein each bit can be configured to divide a position interval in which the vertex currently locates 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 is equal to 2, the position information in the vertex information is 2 bits. The first bit is configured to indicate that the position interval in which the vertex locates 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; and 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; and when the value of the second bit is 1, the vertex is in the second right interval.

[0109] In some embodiments, when N is equal to 2, the position information in the vertex information is 2 bits. The first bit is configured to indicate that the position interval in which the vertex locates 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; and 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; and when the value of the second bit is 1, the vertex is in the second position interval, wherein a quantization distance from the first position interval to a midpoint of the position interval in which the vertex locates is less than a quantization distance from the second position interval to the midpoint of the position interval in which the vertex locates.

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

[0111] FIG. 5 shows an exemplary block diagram of an encoder 500 using an optimal binarization with update on the fly (OBUF) technique for encoding in some embodiments of the present application. In some embodiments, the OBUF technique can quickly and accurately map some column discrete states of the field information to a fixed number M of adaptive entropy encoders through a mapping relationship look-up table (LUT). The above operation can make the information state of the context state tree no longer one-to-one corresponding to the context probability model, but will update the fixed number of context probability models with the syntax element of the current encoding vertex, and update its mapping relationship after each placeholder code encoding is completed, which can reduce the number of context probability models.

[0112] As shown in FIG. 5, in some embodiments, the encoder 500 can include a context state tree determination module 501; in some embodiments, the encoder 500 can include a mapping module 502; in some embodiments, the encoder 500 can 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 can be and / or include the G-PCC encoder 123 and the geometry encoding unit 210 described in conjunction with FIG. 1 and FIG. 2.

[0113] In some embodiments, the context state tree determination module 501 can be configured to determine a context state tree of the to-be-encoded symbol. The context state tree of the to-be-encoded symbol is composed of position information of already-encoded neighboring vertices in space, and the context state tree can be configured to reflect the context state of the to-be-encoded vertex. In some embodiments, when the to-be-encoded symbol is a value of a first bit in position information of the to-be-encoded vertex, the context state tree of the to-be-encoded symbol is a value of the first bit in position information of the already-encoded neighboring vertex. In some embodiments, when the to-be-encoded symbol is a value of a second bit in position information of the to-be-encoded vertex, the context state tree of the to-be-encoded symbol is a value of the second bit in position information of the already-encoded neighboring vertex. In some embodiments, context state tree vertices for constructing the context state tree of the to-be-encoded vertex can be found from 12 neighboring vertices. FIG. 6 shows an example diagram of neighboring vertices of a to-be-encoded edge located in x, y, z axes. As shown in FIG. 6, the neighboring vertices can include one or more of the following categories: (1) a vertex on an edge in a negative direction of an axis corresponding to the to-be-encoded edge; (2) a vertex on a neighboring edge (direct contact) perpendicular to the to-be-encoded edge; (3) a vertex on a neighboring edge (non-direct contact) perpendicular to the to-be-encoded edge; and (4) a vertex on a neighboring edge parallel to the to-be-encoded edge.

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

[0115] In some embodiments, each context state tree vertex can exist or not exist, i.e., a 1, 0 state. The context state tree can represent state information of the K+L context state tree vertices in the context state tree, i.e., the context state tree can be represented by K+L bits. The state information of the K+L context state tree vertices can include K primary information and L secondary information. In the encoding process, the encoder can record the number of times each context state tree is used. When the number of times a certain context state tree is used is greater than a threshold T (for example, 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 the subsequent encoding process, the updated context state tree can be used for geometry coding when encoding the to-be-encoded vertex.

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

[0117] In some embodiments, the mapping module 502 can be configured to determine a context probability model index i corresponding to the context state tree of the to-be-encoded symbol based on the mapping relationship. In some embodiments, the mapping module 502 can determine the context probability model index i corresponding to the context state tree based on the context state tree and a mapping relationship lookup table (LUT) before all to-be-encoded symbols are encoded. The context probability model index i can correspond to a 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 can include one context probability model group, which can correspond to 32 context probability models (i.e., i takes values [0, 1, …, 31]). In some embodiments, the above-mentioned encoder can also be an entropy encoder. i (i takes values [0, 1, …, 31]). In some embodiments, the above-mentioned encoder can also be an entropy encoder.

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

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

[0120] FIG. 7 illustrates an example block diagram of an encoding module 700 that can be configured to perform inter-prediction based Trisoup geometry coding, according to some embodiments of the application. In some embodiments, the encoding module 700 can be and / or include the G-PCC encoder 113, the geometry coding unit 210, and the encoding module 503 described in connection with FIG. 1, FIG. 2, and FIG. 5. In some embodiments, the encoding module 700 can be configured to encode a first bit in the position information of the vertices to be encoded.

[0121] As described in connection with FIG. 7, in some embodiments, the encoding module 700 can include any suitable components that can be configured to perform inter-prediction based Trisoup geometry coding.

[0122] In some embodiments, the encoding module 700 can include a prediction mode determination module 701; wherein the prediction mode determination module 701 can be configured to determine a prediction mode of the symbols to be encoded. In some embodiments, the prediction mode can include intra-prediction; in some embodiments, the prediction mode can include inter-prediction, intra-prediction refers to constructing a context state tree of the vertices to be encoded using intra information to enable prediction of the position information of the vertices to be encoded, inter-prediction refers to constructing a context state tree of the vertices to be encoded using at least inter information to enable prediction of the position information of the vertices to be encoded. In some embodiments, the prediction mode determination module 701 can determine the prediction mode based on a first syntax element and an uncompensated reference vertex information, wherein the first syntax element indicates whether inter-prediction is enabled, denoted as isInter, and the uncompensated reference vertex information indicates whether there is an uncompensated reference vertex, denoted as colocatedVertex. In some embodiments, when the first syntax element indicates that inter-prediction is not enabled or the uncompensated reference vertex information indicates that there is no uncompensated reference vertex, the prediction mode determination module 701 can determine that the prediction mode is an intra-prediction mode. In some embodiments, isInter is 1, indicating that inter-prediction is enabled, and isInter is 0, indicating that inter-prediction is not enabled. In some embodiments, isInter is 0, indicating that inter-prediction is enabled, and isInter is 1, indicating that inter-prediction is not enabled. In some embodiments, colocatedVertex is not equal to 0, indicating that there is an uncompensated reference vertex, and colocatedVertex is equal to 0, indicating that there is no uncompensated reference vertex.

[0123] In some embodiments, when the first syntax element indicates that inter prediction is enabled and the uncompensated reference vertex information indicates that there is an uncompensated reference vertex, the prediction mode determining module 701 can determine the prediction mode of the to-be-encoded vertex according to first quantity information and second quantity information, where the first quantity information indicates a number of times of inaccurate prediction when a second bit of a neighbor uncompensated reference vertex predicts a neighbor vertex, denoted as nBadPredRef2, which can be obtained by counting the encoded neighbor vertex and the uncompensated reference vertex. The second quantity information indicates a number of times of inaccurate prediction when a first bit of a neighbor compensated reference vertex predicts a neighbor vertex, denoted as nBadPredComp1, which can be obtained by counting the encoded neighbor vertex and the compensated reference vertex. In some embodiments, when 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 neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the to-be-encoded vertex after the inter prediction is enabled meets a preset condition. When 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 neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the to-be-encoded vertex after the inter prediction is enabled does not meet the preset condition. In some embodiments, the preset condition can include that the prediction effect of the neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the to-be-encoded vertex is good, for example, the prediction result of the neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the to-be-encoded vertex is accurate or a small number of inaccurate. For example, in some embodiments, when the first quantity information is less than or equal to 0 or the second quantity information is less than or equal to 4, it indicates that the prediction effect of the neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the to-be-encoded vertex after the inter prediction is enabled meets the preset condition. When the first quantity information is greater than 0 and the second quantity information is greater than 4, it indicates that the prediction effect of the neighbor compensated reference vertex and the neighbor uncompensated reference vertex information of the to-be-encoded vertex after the inter prediction is enabled does not meet the preset condition. In some embodiments, 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 determining module 701 can be configured to determine the prediction mode as an inter 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 determining module 701 can be configured to determine the prediction mode as an intra prediction mode.

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

[0125] In some embodiments, the inter context state tree can be divided into an inter context state tree one, an inter context state tree two, an inter context state tree three, and an inter context state tree four. And based on the first bit of the compensation reference vertex, it is predicted that the position interval where the to-be-encoded vertex is located is a first left interval or a first right interval. When the to-be-encoded vertex 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 to-be-encoded vertex 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 where the to-be-encoded vertex is located, 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 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. Based on the first bit of the compensation reference vertex, the position interval in which the to-be-encoded vertex is located is predicted to be a left interval or a right interval. When the to-be-encoded vertex is in the left interval, the second bit divides the left interval into a first position interval and a second position interval. When the to-be-encoded vertex is in the 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 position interval in which the to-be-encoded vertex is located is less than the quantization distance from the second position interval to the midpoint of the position interval in which the to-be-encoded vertex is located. In some embodiments, when the first indication information is not located in the position interval in which the to-be-encoded vertex is located, 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 the context state tree to be 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 the context state tree to be an inter-frame context state tree one; when the first indication information is 2, the context state tree determination module 702 can be configured to determine the context state tree to be an inter-frame context state tree two; when the first indication information is 3, the context state tree determination module 702 can be configured to determine the context state tree to be an inter-frame context state tree three; and when the first indication information is 4, the context state tree determination module 702 can be configured to determine the context state tree to be an inter-frame context state tree four. In this way, the first indication information classifies the context state tree, so that the respective update probabilities of the context state trees do not affect each other, which helps to better predict the probability of the to-be-encoded symbol and improve the coding performance.

[0127] In some embodiments, when the first indication information is 4, the context state tree determining module 702 can be configured to determine the context state tree as inter context state tree one in the inter context state trees; when the first indication information is 3, the context state tree determining module 702 can be configured to determine the context state tree as inter context state tree two in the inter context state trees; when the first indication information is 2, the context state tree determining module 702 can be configured to determine the context state tree as inter context state tree three in the inter context state trees; when the first indication information is 2, the context state tree determining module 702 can be configured to determine the context state tree as inter context state tree four in the inter context state trees.

[0128] In some embodiments, the secondary information in the intra context state tree can include 15 bits of information. The secondary information in the inter context state tree one, inter context state tree two, inter context state tree three, inter context state tree four can include 17 bits of information respectively. The structures of the intra context state tree and the inter context state tree one, inter context state tree two, inter context state tree three, inter context state tree four can be referred to FIG. 8 respectively.

[0129] In some embodiments, the encoding module 700 can include a prediction module 703; wherein the prediction module 703 can be configured to determine the prediction value of the first bit when the prediction mode is the inter prediction mode. In some embodiments, the prediction module 703 can include one or more prediction units 703-1, 703-2, …, 703-G, wherein G = 4. In some embodiments, G can be determined based on the number N of bits 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 can determine the prediction value based on the third quantity information and the uncompensated reference vertex information, where the third quantity information indicates a number of times of incorrect prediction when a second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted, denoted as nBadPredRef2. In some embodiments, the neighbor uncompensated reference vertex prediction neighbor vertex information has the same number of bits in the position information as the position information in the vertex information to be encoded. In some embodiments, when the third quantity information indicates that the number of times of incorrect prediction when the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted is greater than a first threshold, it indicates that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information does not satisfy a preset condition. When the third quantity information indicates that the number of times of incorrect prediction when the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted is less than or equal to the first threshold, it indicates that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information satisfies the preset condition. In some embodiments, the preset condition can include that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is good, 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 a small number of inaccurate. In some embodiments, when the third quantity information indicates that the number of times of incorrect prediction when the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted is greater than 0, it indicates that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information does not satisfy the preset condition. When the third quantity information indicates that the number of times of incorrect prediction when the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted is less than or equal to 0, it indicates that the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information satisfies the preset condition.

[0131] In some embodiments, when the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information does not satisfy the 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 neighbor vertex of the to-be-encoded vertex is not so good, and the one or more prediction units can be configured to determine not to use the uncompensated reference vertex information for prediction, that is, the prediction value is NoPred (i.e., No Prediction). In some embodiments, when the prediction effect of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information satisfies the preset condition and the uncompensated reference vertex information indicates that there is 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 that the prediction value is 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 can be configured to determine that the prediction value is 1 (i.e., Pred 1).

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

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

[0134] In some embodiments, the encoding module 700 can include an entropy encoder 705. The entropy encoder 705 can be configured to entropy encode the to-be-encoded symbols based on the context probability models.

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

[0136] In some embodiments, for the context state tree being the inter context state tree one, when the prediction value is no prediction, the context probability model group 704 can employ the context probability model group 704-2; when the prediction value is prediction 0, the context probability model group 704 can employ the context probability model group 704-3; and when the prediction value is prediction 1, the context probability model group 704 can employ the context probability model group 704-4. For the context state tree being the inter context state tree two, when the prediction value is no prediction, the context probability model group 704 can employ the context probability model group 704-5; when the prediction value is prediction 0, the context probability model group 704 can employ the context probability model group 704-6; and when the prediction value is prediction 1, the context probability model group 704 can employ the context probability model group 704-7. Similarly, when the context state tree is the inter context state tree three and the inter context state tree four, respectively, the context probability model groups 704 employed by the context probability model groups 704 can be determined in turn. In the above cases, the intra context state tree and the inter 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 as shown in FIG. 9.

[0137] In some embodiments, each of the context probability model groups 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 a corresponding context probability model index based on the determined context state tree, where the context probability model index can 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 groups 704 use 13 context probability model groups to select a suitable context probability model group for each context state tree, where the suitable context probability model group refers to the context probability model group corresponding to the prediction value corresponding to the context state tree. The above method can avoid different prediction values of the context state tree sharing the same context probability model group, thereby improving the efficiency of context probability model group probability updating, thereby improving the prediction effect.

[0139] In some embodiments, in one or more prediction units in the prediction module 703, after determining the prediction 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 the fourth quantity information and the fifth quantity information, where the fourth quantity information can indicate the cumulative sum of the first bit in the position information of the coded vertex determined as prediction (i.e., the prediction value is 0 or 1), which can be denoted as PredNum, and the fourth quantity information represents the number of coded vertices determined as prediction, i.e., the number of coded vertices with prediction value of 0 and 1, and the fifth quantity information indicates the cumulative sum of the first bit in the position information of the coded vertex, which can be denoted as CoNum, and the fifth quantity information represents the number of coded vertices with prediction value of 1. In some embodiments, one or more prediction units in the prediction module 703 can determine the prediction value again by comparing the ratio of the fourth quantity information and the fifth quantity information with a preset threshold.

[0140] In some embodiments, if the ratio of the fourth quantity information and the fifth quantity information is greater than the preset threshold value, it can be considered that the case of selecting a neighbor vertex for prediction is more, and the reference information is more similar to the vertex to be encoded, based on the prediction value being 0 or the prediction value being 1. One or more prediction units in the prediction module 703 can be configured to determine that the prediction value is unchanged, i.e., based on the ratio of the fourth quantity information and the fifth quantity information being greater than the preset threshold value. If the prediction value is 0, one or more prediction units in the prediction module 703 can be configured to determine that the prediction value is 0. If the prediction value is 1, one or more prediction units in the prediction module 703 can be configured to determine that the prediction value is 1. In some embodiments, if the ratio of the fourth quantity information and the fifth quantity information is less than or equal to the preset threshold value, it can be considered that the reference information is not similar enough to the vertex to be encoded, based on the prediction value being 0 or the prediction value being 1. One or more prediction units in the prediction module 703 can be configured to determine that the updated prediction value is not predicted, i.e., based on the ratio of the fourth quantity information and the fifth quantity information being less than or equal to the preset threshold value. If the prediction value is 0 or 1, one or more prediction units in the prediction module 703 can be configured to determine that the updated prediction value is not predicted. In this way, by comparing the ratio of the fourth quantity information and the fifth quantity information with the preset threshold value, it can be determined whether to update the prediction value and the updated prediction value when updating, and a better prediction effect can be obtained.

[0141] In some embodiments, the budget value determined by one or more prediction units in the prediction module 703 can include the following cases: 1. not predicted; 2. the prediction value before updating is 0, and the prediction value after updating is not predicted; 3. the prediction value before updating is 1, and the prediction value after updating is not predicted; 4. the prediction value is 0; 5. the prediction value is 1.

[0142] The above-mentioned various cases correspond to different context probability model groups 704-i in the context probability model groups 704. In some embodiments, based on the inter-frame context state tree one, case 1 above corresponds to the context probability model group 704-2 in the context probability model groups 704, i.e., when the prediction value is not predicted, the inter-frame context state tree one corresponds to the context probability model group 704-2 in the context probability model groups 704; case 2 above corresponds to the context probability model group 704-3 in the context probability model groups 704, i.e., when the prediction value is 0 before updating and not predicted after updating, the inter-frame context state tree one corresponds to the context probability model group 704-3 in the context probability model groups 704; case 3 above corresponds to the context probability model group 704-4 in the context probability model groups 704, i.e., when the prediction value is 1 before updating and not predicted after updating, the inter-frame context state tree one corresponds to the context probability model group 704-4 in the context probability model groups 704; case 4 above corresponds to the context probability model group 704-3 in the context probability model groups 704, i.e., when the prediction value is predicted 0, the inter-frame context state tree one corresponds to the context probability model group 704-3 in the context probability model groups 704; and case 5 above corresponds to the context probability model group 704-4 in the context probability model groups 704, i.e., when the prediction value is predicted 1, the inter-frame context state tree one corresponds to the context probability model group 704-4 in the context probability model groups 704. For the above-mentioned cases, the secondary information of the intra-frame context state tree can include 15 bits of information, and the inter-frame context state tree one, two, three, and four can each include 16 bits of information. The secondary information structure of the context state tree can be as shown in FIG. 10.

[0143] In summary, although one or more prediction units in the prediction module 703 update the partial prediction value 0 and the prediction value 1 to the prediction value not predicted, 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 prediction value before updating. This approach can combine some inter-frame context state trees, avoiding slow convergence of the probability corresponding to the inter-frame context state tree when the use of inter-frame prediction is less, thereby having no good prediction effect.

[0144] In one example, the encoding process of the to-be-encoded symbol by the embodiments of the present application is described in detail with the number of bits N = 2 of the position information, the to-be-encoded symbol being the first bit in the position information of the to-be-encoded vertex, and the number of the inter-frame context state tree model group H = 13. Specifically, in the case that the inter-frame prediction is enabled (i.e., isInter = 1) and the to-be-encoded vertex has an un-compensated reference vertex (i.e., colocatedVertex ≠ 0), the encoder can obtain the first number information nBadPredRef2 of the number of incorrect predictions when the second bit of the neighbor un-compensated reference vertex predicts the neighbor vertex, obtain the second number information nBadPredComp1 of the number of incorrect predictions when the first bit of the neighbor compensated reference vertex predicts the neighbor vertex, and determine the prediction mode as the inter-frame prediction mode in the case that the first number information nBadPredRef2 is less than or equal to a first threshold (e.g., 0) or the second number information nBadPredComp1 is less than or equal to a second threshold (e.g., 4). Then, the value of the first indication information ctxInter is determined according to the position information of the compensated reference vertex, and the context state tree of the to-be-encoded vertex is determined to be the inter-frame context state tree one, the inter-frame context state tree two, the inter-frame context state tree three, or the inter-frame context state tree four according to the value of the first indication information, wherein the secondary information of each inter-frame context state tree can include 17 bits of information. Then, the encoder can obtain the third number information nBadPredRef2 of the number of incorrect predictions when the second bit of the neighbor un-compensated reference vertex predicts the neighbor vertex information and the information colocatedVertex indicating whether there is an un-compensated reference vertex, and the encoder can not use the un-compensated reference vertex information for prediction, i.e., no prediction (i.e., NoPred) in the case that the third number information nBadPredRef2 is greater than a threshold (e.g., 0) or the un-compensated reference vertex information colocatedVertex indicates that there is no un-compensated reference vertex. In some embodiments, when the prediction effect of the first bit of the neighbor un-compensated reference vertex predicting the neighbor vertex information meets a preset condition and the un-compensated reference vertex information indicates that there is an un-compensated reference vertex, the encoder determines the prediction value based on the first bit in the position information of the un-compensated reference vertex. In some embodiments, if the first bit in the position information of the un-compensated reference vertex is 0, the one or more prediction units determine the prediction value as 0 (i.e., Pred 0). If the first bit in the position information of the un-compensated reference vertex is 1, the one or more prediction units determine the prediction value as 1 (i.e., Pred 1).

[0145] In some embodiments, the encoder can further determine the prediction value for the prediction value of 0 and the prediction value of 1 based on a ratio of a fourth quantity information and a fifth quantity information, wherein the fourth quantity information indicates a cumulative sum of the first bits in the position information of the encoded vertices determined to be predicted, and the fifth quantity information indicates a cumulative sum of the first bits in the position information of the encoded vertices. In some embodiments, the encoder can determine the prediction value for the prediction value of 0 and the prediction value of 1 by comparing the ratio of the fourth quantity information and the fifth quantity information with a preset threshold. For example, based on the prediction value of 0 or the prediction value of 1, if the ratio of the fourth quantity information and the fifth quantity information is greater than the preset threshold, the encoder determines that the prediction value is unchanged, i.e., based on the ratio of the fourth quantity information and the fifth quantity information being greater than the preset threshold, if the prediction value is 0, the encoder determines that the prediction value is 0; if the prediction value is 1, the encoder determines that the prediction value is 1. In some embodiments, based on the prediction value of 0 or the prediction value of 1, if the ratio of the fourth quantity information and the fifth quantity information is less than or equal to the preset threshold, the encoder can determine that the updated prediction value is not predicted, i.e., based on the ratio of the fourth quantity information and the fifth quantity information being less than or equal to the preset threshold, if the prediction value is 0 or 1, the encoder determines that the updated prediction value is not predicted. In the above cases, the prediction value determined by the encoder can include the following cases: 1. not predicted; 2. the prediction value before updating is 0, and the prediction value after updating is not predicted; 3. the prediction value before updating is 1, and the prediction value after updating is not predicted; 4. the prediction value is 0; 5. the prediction value is 1.

[0146] In 1307, the encoder can determine a context probability model based on the context state tree and the prediction value corresponding to the to-be-encoded symbol. In some embodiments, the encoder can include one or more context probability model groups 1 - context probability model group H, where, for example, H = 5. That is, the encoder can include context probability model groups 1 - 5. Based on the prediction mode being an intra prediction mode, the context probability model group 1 can be configured to determine a context probability model based on an intra context state tree. Based on the prediction mode being an inter prediction mode, the context probability model group 2 can be configured to determine a context probability model based on an inter context state tree 1 and its corresponding prediction value for subsequent encoding, the context probability model group 3 can be configured to determine a context probability model based on an inter context state tree 2 and its corresponding prediction value for subsequent encoding, the context probability model group 4 can be configured to determine a context probability model based on an inter context state tree 3 and its corresponding prediction value for subsequent encoding, and the context probability model group 5 can be configured to determine a context probability model based on an inter context state tree 4 and its corresponding prediction value, respectively. In some embodiments, when the N is other values, the encoder can set the corresponding inter context state trees and the corresponding context probability model groups according to the value of N, and determine the context probability model based on the corresponding inter context state trees and the corresponding context probability model groups. For example, when N = 3, 8 inter context state trees and the corresponding context probability model groups can be set.

[0147] In some embodiments, the encoder can include one or more sets of context probability models, Set 1-Set H, where H=13. In some embodiments, H can be determined based on the number of bits of the position information in the vertex to be encoded. That is, the encoder can include Set 1-Set 13 of context probability models. Based on the prediction mode being an intra prediction mode, Set 1 of the context probability models can be configured to determine a context probability model based on an intra context state tree. Based on the prediction mode being an inter prediction mode, when the context state tree is Inter Context State Tree One and the prediction value is no prediction, Set 2 of the context probability models can be configured to determine a context probability model. When the context state tree is Inter Context State Tree One and the prediction value is prediction 0, Set 3 of the context probability models can be configured to determine a context probability model. When the context state tree is Inter Context State Tree One and the prediction value is prediction 1, Set 4 of the context probability models can be configured to determine a context probability model. When the context state tree is Inter Context State Tree Two, Three, Four, Set 5-Set 13 of the context probability models can be configured to determine a context probability model in the same order as for Inter Context State Tree One. In some embodiments, when N is other values, then the encoder can set the corresponding inter context state tree and the corresponding set of context probability models according to the value of N, and determine the context probability model based on the corresponding inter context state tree and the corresponding set of context probability models.

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

[0149] In some embodiments, each of the context probability model groups 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 the determined context state tree, where the context probability model index can 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 to-be-encoded symbol based on the context probability model.

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

[0152] In some embodiments, the encoding method described in the present application can merge part of the inter-frame context state tree, avoiding the problem that the probability corresponding to the inter-frame context state tree converges slowly when the use of inter-frame prediction is less, thereby having no good prediction effect. In addition, the encoding method described in the present application can also share the same context probability model group for different prediction values, thereby improving the efficiency of the context probability model group probability update.

[0153] FIG. 14 shows an exemplary flowchart of a decoding method 1400 of Trisoup geometry decoding based on inter-frame prediction according to some embodiments of the present application. In some embodiments, the execution subject of the decoding method can be a decoder. The decoder can be the G-PCC decoder 123 and the G-PCC decoder 300 described in combination with FIG. 1 and FIG. 3.

[0154] As shown in FIG. 14, in 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 to enable inter-frame prediction, and the uncompensated reference vertex information indicates whether there is an uncompensated reference vertex. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled or the uncompensated reference vertex information indicates that there is no uncompensated reference vertex, the decoder can determine that the prediction mode is an intra-frame prediction mode, i.e., the decoding method flowchart can proceed to 1403.

[0155] At 1402, the decoder can determine the prediction mode based on the first number information and the second number information, wherein the first number information indicates a number of times of inaccurate prediction when a neighbor non-compensated reference vertex predicts a neighbor vertex by a second number of bits, denoted as nBadPredRef2, which can be obtained by counting the neighbor vertices and the non-compensated reference vertices. The second number information indicates a number of times of inaccurate prediction when a neighbor compensated reference vertex predicts a neighbor vertex by a first number of bits, denoted as nBadPredComp1, which can be obtained by counting the neighbor vertices and the compensated reference vertices. In some embodiments, when the first number information is less than or equal to a first threshold or the second number information is less than or equal to a second threshold, it indicates that the prediction effect of the neighbor compensated reference vertex and the neighbor non-compensated reference vertex information of the to-be-encoded vertex after enabling inter-prediction satisfies a preset condition. When the first number information is greater than the first threshold and the second number information is greater than the second threshold, it indicates that the prediction effect of the neighbor compensated reference vertex and the neighbor non-compensated reference vertex information of the to-be-encoded vertex after enabling inter-prediction does not satisfy the preset condition. In some embodiments, the preset condition can include that the prediction effect of the neighbor compensated reference vertex and the neighbor non-compensated reference vertex information of the to-be-encoded vertex is good, for example, the prediction result of the neighbor compensated reference vertex and the neighbor non-compensated reference vertex information of the to-be-encoded vertex is accurate or a small number of inaccurate. For example, in some embodiments, when the first number information is less than or equal to 0 or the second number information is less than or equal to 4, it indicates that the prediction effect of the neighbor compensated reference vertex and the neighbor non-compensated reference vertex information of the to-be-decoded vertex after enabling inter-prediction satisfies the preset condition. When the first number information is greater than 0 and the second number information is greater than 4, it indicates that the prediction effect of the neighbor compensated reference vertex and the neighbor non-compensated reference vertex information of the to-be-decoded vertex after enabling inter-prediction does not satisfy the preset condition. In some embodiments, based on the first number information being less than or equal to the first threshold or the second number information being less than or equal to the second threshold, the decoder determines that the prediction mode is an inter-prediction mode, i.e., the decoding method flow can proceed to 1404. Based on the first number information being greater than the first threshold and the second number information being greater than the second threshold, the prediction mode determination module 701 can be configured to determine that the prediction mode is an intra-prediction mode, i.e., the decoding method flow can proceed to 1403.

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

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

[0158] In 1405, the decoder can determine the context state tree based on first indication information, wherein the first indication information indicates position information of a compensated reference vertex. In some embodiments, the inter-frame context state tree can be divided into 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. And based on the first bit of the compensated reference vertex, it is predicted that the position interval where the to-be-decoded vertex is located is a first left interval or a first right interval. When the to-be-decoded vertex 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 represents not being located in the position interval where the to-be-decoded vertex is located, the first indication information is 0; when the first indication information represents being located in the second left interval, the first indication information is 1; when the first indication information represents being located in the second right interval, the first indication information is 2; when the first indication information represents being located in the third left interval, the first indication information is 3; and when the first indication information represents being 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 an 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 an 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 an inter-frame context state tree three in the inter-frame context state tree; and when the first indication information is 4, the decoder determines that the context state tree is an inter-frame context state tree four in the inter-frame context state tree.

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

[0162] At 1406, the decoder can determine a predicted value of the symbol to be decoded. In some embodiments, the decoder can determine the predicted value based on the third number information and the uncompensated reference vertex information, where the third number information indicates a number of times a second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted incorrectly, which can be denoted as nBadPredRef2. In some embodiments, when the third number information indicates that the number of times the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted incorrectly is greater than a first threshold, it indicates that the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information does not satisfy a preset condition. When the third number information indicates that the number of times the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted incorrectly is less than or equal to the first threshold, it indicates that the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information satisfies the preset condition. In some embodiments, the preset condition can include that the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is good, for example, the prediction results of the neighbor compensated vertex and the neighbor uncompensated vertex information of the vertex to be encoded are accurate or a small number of inaccurate. In some embodiments, when the third number information indicates that the number of times the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted incorrectly is greater than 0, it indicates that the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information does not satisfy the preset condition. When the third number information indicates that the number of times the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information is predicted incorrectly is less than or equal to 0, it indicates that the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information satisfies the preset condition. In some embodiments, when the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information does not satisfy the 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, i.e., the predicted value is not predicted (i.e., NoPred). In some embodiments, when the prediction of the second bit in the neighbor uncompensated reference vertex prediction neighbor vertex information satisfies the preset condition and the uncompensated reference vertex information indicates that there is an uncompensated reference vertex, the decoder determines the predicted value based on a 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, the one or more prediction units determine that the predicted value is 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 that the predicted value is 1 (i.e., Pred 1).

[0163] In some embodiments, the decoder can determine the prediction value of the prediction value of 0 and the prediction value of 1 based on a ratio of a fourth quantity information and a fifth quantity information, wherein the fourth quantity information indicates a cumulative sum of the first bit in the position information of the decoded vertex, denoted as PredNum, and the fourth quantity information represents the number of the encoded vertex determined as the prediction, i.e., the number of the encoded vertex of the prediction value of 0 and 1, the fifth quantity information indicates a cumulative sum of the first bit in the position information of the decoded vertex, denoted as CoNum, and the fifth quantity information represents the number of the encoded vertex of the prediction value of 1. In some embodiments, the decoder can determine the prediction value of the prediction value of 0 and the prediction value of 1 by comparing the ratio of the fourth quantity information and the fifth quantity information with a preset threshold. For example, based on the prediction value of 0 or the prediction value of 1, if the ratio of the fourth quantity information and the fifth quantity information is greater than the preset threshold, the decoder determines that the prediction value is unchanged, i.e., based on the ratio of the fourth quantity information and the fifth quantity information being greater than the preset threshold, if the prediction value is 0, the decoder determines that the prediction value is 0; if the prediction value is 1, the decoder determines that the prediction value is 1. In some embodiments, based on the prediction value of 0 or the prediction value of 1, if the ratio of the fourth quantity information and the fifth quantity information is less than or equal to the preset threshold, the decoder determines that the updated prediction value is not predicted, i.e., based on the ratio of the fourth quantity information and the fifth quantity information being less than or equal to the preset threshold, if the prediction value is 0 or 1, the decoder determines that the updated prediction value is not predicted. In the above cases, the prediction value determined by the decoder can include the following cases: 1. not predicted; 2. the prediction value before updating is 0, and the prediction value after updating is not predicted; 3. the prediction value before updating is 1, and the prediction value after updating is not predicted; 4. the prediction value is 0; 5. the prediction value is 1.

[0164] In 1407, the decoder can determine a context probability model for the to-be-decoded symbol based on the context state tree and the prediction value for subsequent decoding. In some embodiments, the decoder can include one or more context probability model groups 1 - context probability model group H, where H = 5. That is, the decoder can include context probability model groups 1 - 5. Based on the prediction mode being an intra prediction mode, the context probability model group 1 can be configured to determine a context probability model for subsequent decoding based on an intra context state tree. Based on the prediction mode being an inter prediction mode, the context probability model groups 2 - 5 can be configured to determine a context probability model for subsequent decoding based on an inter context state tree one, an inter context state tree two, an inter context state tree three, an inter context state tree four, and their corresponding prediction values, respectively. In some embodiments, when the N is other values, then the decoder can set the corresponding inter context state tree and the corresponding context probability model group according to the value of N, and determine a context probability model for subsequent decoding based on the corresponding inter context state tree and the corresponding context probability model group.

[0165] In some embodiments, the decoder can include one or more context probability model groups 1 - context probability model group H, where H = 13. In some embodiments, H can be determined based on the number of bits of the position information in the to-be-decoded vertex. The decoder can include context probability model groups 1 - 13. Based on the prediction mode being an intra prediction mode, the context probability model group 1 can be configured to determine a context probability model for subsequent decoding based on an intra context state tree. Based on the prediction mode being an inter prediction mode, when the context state tree is an inter context state tree one and the prediction value is no prediction, the context probability model group 2 can be configured to determine a context probability model for subsequent decoding. When the context state tree is an inter context state tree one and the prediction value is prediction 0, the context probability model group 3 can be configured to determine a context probability model for subsequent decoding. When the context state tree is an inter context state tree one and the prediction value is prediction 1, the context probability model group 4 can be configured to determine a context probability model for subsequent decoding. When the context state tree is an inter context state tree two, three, or four, the context probability model groups 5 - 13 can be configured to be binary-decoded in sequence as in the case of the inter context state tree one. In some embodiments, when the N is other values, then the decoder can set the corresponding inter context state tree and the corresponding context probability model group according to the value of N, and determine a context probability model for subsequent decoding based on the corresponding inter context state tree and the corresponding context probability model group.

[0166] In some embodiments, when the prediction value determined by the decoder is in the above five cases, based on the context state tree being the inter-frame context state tree one, case 1 above corresponds to the context probability model group 2, i.e. when the prediction value is not predicted, the inter-frame context state tree one corresponds to the context probability model group 2 in the decoder; case 2 above corresponds to the context probability model group 3 in the decoder, i.e. when the prediction value is 0 before updating and not predicted after updating, the inter-frame context state tree one corresponds to the context probability model group 3 in the decoder; case 3 above corresponds to the context probability model group 4 in the decoder, i.e. when the prediction value is 1 before updating and not predicted after updating, the inter-frame context state tree one corresponds to the context probability model group 4 in the decoder; case 4 above corresponds to the context probability model group 3 in the decoder, i.e. when the prediction value is 0, the inter-frame context state tree one corresponds to the context probability model group 3 in the decoder; case 5 above corresponds to the context probability model group 4 in the decoder, i.e. when the prediction value is 1, the inter-frame context state tree one corresponds to the context probability model group 4 in the decoder. When the context state tree is the inter-frame context state tree two, three, four, the context probability model group 5-context probability model group 13 can be sequentially inferred to be configured as binary decoding according to the case of the inter-frame context state tree one.

[0167] In some embodiments, each of the context probability model groups 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 the determined context state tree, where the context probability model index can 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 1408, the decoder decodes the to-be-decoded symbol based on the context probability model.

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

[0170] In one exemplary example, the decoding process of the to-be-decoded symbol by the embodiment of the present application is described in detail with the number of bits N = 2 of the position information, the to-be-decoded symbol being the first bit in the position information of the to-be-decoded vertex, and the number of the inter-frame context state tree model groups H = 13. Specifically, in the case that the inter-frame prediction is enabled (i.e., isInter = 1) and the to-be-decoded vertex has an uncompensated reference vertex (i.e., colocatedVertex ≠ 0), the decoder can obtain the first number information nBadPredRef2 of the number of incorrect predictions when the second bit of the neighbor uncompensated reference vertex predicts the neighbor vertex, obtain the second number information nBadPredComp1 of the number of incorrect predictions when the first bit of the neighbor compensated reference vertex predicts the neighbor vertex, and determine that the prediction mode is the inter-frame prediction mode in the case that the first number information nBadPredRef2 is less than or equal to a first threshold (e.g., 0) or the second number information nBadPredComp1 is less than or equal to a second threshold (e.g., 4). Then, the value of the first indication information ctxInter is determined according to the position information of the compensated reference vertex, and the context state tree of the to-be-decoded vertex is determined to be the first inter-frame context state tree, the second inter-frame context state tree, the third inter-frame context state tree, or the fourth inter-frame context state tree according to the value of the first indication information, wherein the secondary information of each inter-frame context state tree can include 17 bits of information. Then, the decoder can obtain the third number information nBadPredRef2 of the number of incorrect predictions when the second bit of the neighbor uncompensated reference vertex predicts the neighbor vertex information and the information colocatedVertex indicating whether there is an uncompensated reference vertex, and the decoder can not use the uncompensated reference vertex information for prediction, i.e., NoPred, in the case that the third number information nBadPredRef2 is greater than a threshold (e.g., 0) or the uncompensated reference vertex information colocatedVertex indicates that there is no uncompensated reference vertex, and the decoder can determine the prediction value based on the first bit in the position information of the uncompensated reference vertex in the case that the third number information nBadPredRef2 is less than or equal to the threshold (e.g., 0) and the uncompensated reference vertex information colocatedVertex indicates that there is an uncompensated reference vertex, wherein if the first bit is 0, the prediction value is 0 (i.e., Pred 0), and if the first bit is 1, the prediction value is 1 (i.e., Pred 1). Finally, the decoder determines the corresponding context probability model group according to the determined inter-frame context state tree and the prediction value, determines the context probability model based on the corresponding inter-frame context state tree and the corresponding context probability model group, and decodes the to-be-decoded symbol based on the context probability model.

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

[0172] In some embodiments, the processor 1501 can be configured to perform the encoding method and / or the decoding method provided by the embodiments of the present application. The memory 1502 can be configured to store instructions, programs, and / or codes for implementing the encoding method and / or the decoding method provided by the embodiments of the present application. The communication interface 1503 can be configured to enable the hardware structure 1500 to communicate with other devices or can be configured to communicate with internal modules.

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

[0174] In some embodiments, the hardware structure 1500 can include a memory 1502. The memory 1502 can be a device that uses electrical, optical, or other forms of

[0175] In some embodiments, the hardware interface can include a communication interface 1503. The communication interface 1503 can be a wired network interface or a wireless network interface. The communication interface 1503 can include one or more combinations of a metal cable, an optical fiber, a hybrid cable, a connection circuit, or other wired network interfaces. The communication interface 1503 can include one or more combinations of a wireless local area network (WLAN) interface, a local area network (LAN) interface, a wide area network (WAN) interface, a Bluetooth interface, a ZigBee interface, and a near field communication (NFC) interface. In some embodiments, the communication interface 1503 can 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 can be configured to transmit encoded data or decoded data according to a cellular communication standard, such as 4G, 4G-LTE, 5G, etc. In some embodiments, the communication interface 1503 can also be configured to transmit encoded data or decoded data according to other wireless standards, such as IEEE 802.11 specifications, IEEE 802.15 specifications (e.g., ZigBee TM ), Bluetooth TM standards, etc.

[0176] In some embodiments, the method provided by some embodiments of the present application further determines the inter prediction information as information of prediction 0 and prediction 1 based on the sixth quantity information and the seventh quantity information, thereby updating part of the inter prediction information from prediction 0 and prediction 1 to no prediction. The sixth quantity information is the cumulative sum of the second bits in the position information of the coded vertex determined to be position prediction, and the seventh quantity information is the cumulative sum of the second bits in the position information of the coded vertex. In some embodiments, the above operation can merge part of the inter context state tree, thereby avoiding slow convergence of the probability corresponding to the inter context state tree and poor prediction effect when the use of inter prediction is less.

[0177] In some embodiments, the context state tree can include an intra context state tree, an inter context state tree one, and an inter context state tree two. In some embodiments, the context state tree can include more context state trees, for example, the number of context state trees can be determined according to N.

[0178] In some embodiments, the set of context probability models can include 7 sets of context probability models. Each of the inter-frame context state trees corresponds to three prediction values, i.e. no prediction, prediction 0, and prediction 1, respectively. In some embodiments, each of the set of context probability models corresponds to one context state tree and one prediction value. For example, in some embodiments, one of the set of context probability models can be configured to determine a context probability model based on the intra-frame context state tree. In some embodiments, the remaining 6 of the set of context probability models can be configured to determine a context probability model based on the inter-frame context state tree one and the inter-frame context state tree two and their corresponding prediction values. In some embodiments, the method provided in some embodiments of the present disclosure can map the inter-frame context state trees to the corresponding set of context probability models to determine the context probability model based on the prediction value after determining the context state tree for the second bit of the position information in the vertex when performing inter-frame prediction.

[0179] In some embodiments, the G-PCC encoder can be considered to encode the vertex information when encoding the vertex, i.e. to encode the position information of the vertex, i.e. to encode the N bits. In some embodiments, when encoding the second bit of the N bits in the position information of the vertex, the vertex is the vertex to be encoded, and the second bit of the N bits is the symbol to be encoded.

[0180] FIG. 7 shows an exemplary block diagram of an encoding module 700 that can be configured to perform Trisoup geometry 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 the second bit of the position information in the vertex to be encoded.

[0181] In some embodiments, the encoding module 700 can comprise a prediction mode determining module 701. The prediction mode determining module 701 can be configured to determine a prediction mode of the to-be-encoded symbol. In some embodiments, the prediction mode can comprise an intra prediction. In some embodiments, the prediction mode can comprise an inter prediction. In some embodiments, the prediction mode determining module 701 can determine the prediction mode based on a first syntax element and a compensated reference vertex information, wherein the first syntax element can be configured to indicate whether the inter prediction is enabled, and the compensated reference vertex information can be configured to indicate whether there is a compensated reference vertex of the to-be-encoded vertex after the inter prediction mode is enabled. In some embodiments, when the first syntax element indicates that the inter prediction is not enabled or the compensated reference vertex information indicates that there is no compensated reference vertex of the to-be-encoded vertex after the inter prediction mode is enabled, the prediction mode determining module 701 can determine the prediction mode as the intra prediction mode.

[0182] In some embodiments, the inter context state tree can be divided into an inter context state tree one and an inter context state tree two. And based on the first bit of the compensated reference vertex, the position interval where the to-be-encoded vertex is located is predicted to be divided into a first left interval or a first right interval. When the to-be-encoded vertex 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 to-be-encoded 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.

[0183] In some embodiments, the inter context state tree can be divided into an inter context state tree one and an inter context state tree two. And based on the first bit of the compensated reference vertex, the position interval where the to-be-encoded vertex is located is predicted to be divided into a left interval or a right interval. When the to-be-encoded vertex is in the left interval, the second bit divides the left interval into a first position interval and a second position interval. When the to-be-encoded vertex is in the right interval, the second bit divides the right interval into a first position interval and a second position interval. Wherein the quantization distance from the first position interval to the midpoint of the located position interval is less than the quantization distance from the second position interval to the midpoint of the located position interval.

[0184] In some embodiments, when the parity bit information of the compensated reference vertex information is 0, the context state tree determining module 702 can determine the inter context state tree as the inter context state tree one. When the parity bit information of the compensated reference vertex information is 1, the context state tree determining module 702 can determine the inter context state tree as the inter context state tree two.

[0185] In some embodiments, the secondary information in the intra context state tree can include 15 bits of information. The secondary information in the inter context state tree one and the inter context state tree two can include 17 bits of information. The structures of the intra context state tree and the inter context state tree one, inter context state tree two can refer to FIG. 8.

[0186] In some embodiments, the encoding module 700 can include a prediction module 703. The prediction module 703 can be configured to determine a prediction value of the to-be-encoded vertex when the prediction mode is the inter prediction mode. In some embodiments, the prediction module 703 can include one or more prediction units 703-1, 703-2, …, 703-G, where G = 2. In some embodiments, G can also be determined based on the number of bits N of the position information in the to-be-encoded vertex. 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 prediction value based on a second syntax element and the same bit information of the uncompensated reference vertex information of the to-be-encoded vertex, where the value of the second syntax element is determined based on a prediction result of a neighbor uncompensated reference vertex predicting a neighbor vertex second bit, the uncompensated reference vertex information, and a prediction result of an uncompensated reference vertex first bit. In some embodiments, when the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit does not satisfy a preset condition or the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist or the prediction result of the uncompensated reference vertex first bit does not satisfy the preset condition, the value of the second syntax element is not equal to 1; when the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit satisfies the preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the uncompensated reference vertex first bit satisfies the preset condition, the value of the second syntax element is equal to 1. In some embodiments, the preset condition refers to that the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit or the prediction result of the uncompensated reference vertex first bit is good, i.e., the prediction accuracy of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit or the uncompensated reference vertex first bit is high.

[0188] In some embodiments, based on the value of the second syntax element not equal to 1, the one or more prediction units can determine that the to-be-encoded symbol is not predicted using uncompensated reference vertex information, i.e., the prediction value is NoPred. In some embodiments, based on the value of the second syntax element equal to 1, the one or more prediction units can determine the prediction value of the to-be-encoded symbol based on the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex. Based on the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex being 0, the one or more prediction units can determine that the prediction value of the to-be-encoded symbol is 0 (i.e., Pred 0). Based on the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex being 1, the one or more prediction units can determine that the prediction value of the to-be-encoded symbol is 1 (Pred 1).

[0189] In some embodiments, based on the prediction value of the to-be-encoded symbol being 0 or 1, the one or more prediction units can update the prediction value of the to-be-encoded symbol based on sixth quantity information and seventh quantity information, wherein the sixth quantity information is the cumulative sum of the second bit in the position information of the encoded vertex determined to be predicted, and the seventh quantity information is the cumulative sum of the second bit in the position information of the encoded vertex. In some embodiments, the one or more prediction units can update the prediction value of the to-be-encoded symbol 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 can update the prediction value of the to-be-encoded symbol to NoPred; i.e., when the prediction 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 prediction value is updated to NoPred. In some embodiments, based on the ratio of the sixth quantity information and the seventh quantity information being greater than the preset threshold, the prediction value is not updated; i.e., when the prediction 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 prediction value remains the original prediction value of 0 or 1.

[0190] In some embodiments, the budget value determined by the one or more prediction units in the prediction module 703 can include the following cases: 1. NoPred; 2. The prediction value before updating is 0, and the prediction value after updating is NoPred; 3. The prediction value before updating is 1, and the prediction value after updating is NoPred; 4. The prediction value is 0; 5. The prediction value is 1.

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

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

[0193] In some embodiments, the context probability model groups 704 can include one or more context probability model groups 704-1, 704-2, …, 704-H, where H = 7. In some embodiments, H can be determined based on the number of bits of the position information in the vertex to be encoded. In some embodiments, based on the prediction mode being an intra prediction mode, the context probability model group 704-1 can be configured to determine context probability models based on an intra context state tree. In some embodiments, based on the prediction mode being an inter prediction mode, each of the inter context state tree one, inter context state tree two can correspond to three context probability model groups 704-i, 704-i+1, 704-i+2. That is, each inter 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 prediction mode and the context state tree is an inter context state tree, the prediction values 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 groups 704 can include context probability model groups 1-7. For the context state tree being an inter context state tree one, when the prediction value is no prediction, the context probability model groups 704 can employ context probability model group 704-2; when the prediction value is prediction 0, the context probability model groups 704 can employ context probability model group 704-3; and when the prediction value is prediction 1, the context probability model groups 704 can employ context probability model group 704-4. Similarly, for the context state tree being an inter context state tree two, when the prediction value is no prediction, the context probability model groups 704 can employ context probability model group 704-5; when the prediction value is prediction 0, the context probability model groups 704 can employ context probability model group 704-6; and when the prediction value is prediction 1, the context probability model groups 704 can employ context probability model group 704-7. In the above cases, the intra context state tree and the inter context state tree one and two can each include 15 bits of information. The secondary information structure of the context state tree can be as shown in FIG. 9.

[0195] The context probability model group 704 uses 7 context probability model groups to select a suitable context probability model group for each context state tree, wherein the suitable context probability model group refers to the context probability model group corresponding to the prediction value corresponding to the context state tree. The above method can avoid different prediction values of the context state tree sharing the same context probability model group, thereby improving the efficiency of context probability model group probability updating, thereby improving the prediction effect.

[0196] In some embodiments, each of the context probability model groups 704 can 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 the determined context state tree, wherein the context probability model index can indicate a context probability model i in the context probability model group. In some embodiments, based on the context state tree, it can be determined that the context probability model index indicates the context probability model i.

[0197] In some embodiments, the context probability model set 704 can include context probability model set 1-7. Based on the updated situation of the prediction value, the prediction value can include the following cases: 1. no prediction; 2. the prediction value is 0 before updating and no prediction after updating; 3. the prediction value is 1 before updating and no prediction after updating; 4. the prediction value is 0; 5. the prediction value is 1. For the above prediction value, based on the context state tree being the inter context state tree one, the prediction value being no prediction corresponds to the context probability model set 704-2 in the context probability model set 704, i.e. when the prediction value is no prediction, the inter context state tree one corresponds to the context probability model set 704-2 in the context probability model set 704; the prediction value being the prediction value being 0 before updating and no prediction after updating corresponds to the context probability model set 704-3 in the context probability model set 704, i.e. when the prediction value is 0 before updating and no prediction after updating, the inter context state tree one corresponds to the context probability model set 704-3 in the context probability model set 704; the prediction value being the prediction value being 1 before updating and no prediction after updating corresponds to the context probability model set 704-4 in the context probability model set 704, i.e. when the prediction value is 1 before updating and no prediction after updating, the inter context state tree one corresponds to the context probability model set 704-4 in the context probability model set 704; the prediction value being 0 corresponds to the context probability model set 704-3 in the context probability model set 704, i.e. when the prediction value is prediction 0, the inter context state tree one corresponds to the context probability model set 704-3 in the context probability model set 704; the prediction value being 1 corresponds to the context probability model set 704-4 in the context probability model set 704, i.e. when the prediction value is prediction 1, the inter context state tree one corresponds to the context probability model set 704-4 in the context probability model set 704.

[0198] Similarly, for the above prediction values, based on the context state tree being the inter context state tree two, the prediction value is not predicting the context probability model group 704-5 in the context probability model groups 704, i.e., when the prediction value is not predicting, the inter context state tree two corresponds to the context probability model group 704-5 in the context probability model groups 704; the prediction value is 0 before updating and not predicting the context probability model group 704-6 in the context probability model groups 704 after updating, i.e., when the prediction value is 0 before updating and not predicting after updating, the inter context state tree two corresponds to the context probability model group 704-6 in the context probability model groups 704; the prediction value is 1 before updating and not predicting the context probability model group 704-7 in the context probability model groups 704 after updating, i.e., when the prediction value is 1 before updating and not predicting after updating, the inter context state tree two corresponds to the context probability model group 704-7 in the context probability model groups 704; the prediction value is 0 corresponds to the context probability model group 704-6 in the context probability model groups 704, i.e., when the prediction value is predicting 0, the inter context state tree two corresponds to the context probability model group 704-6 in the context probability model groups 704; the prediction value is 1 corresponds to the context probability model group 704-7 in the context probability model groups 704, i.e., when the prediction value is predicting 1, the inter context state tree two corresponds to the context probability model group 704-7 in the context probability model groups 704. For the above cases, the secondary information of the intra context state tree includes 15 bits of information, and the inter context state trees one, two, three and four can each include 16 bits of information. The secondary information structure of the context state tree can be as shown in FIG. 10.

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

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

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

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

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

[0204] FIG. 12 illustrates an exemplary flowchart of an encoding method flow 1200 using a dynamic updating best binarization technique according to some embodiments of the present application. In some embodiments, the execution subject of the encoding method can be an encoder. The encoder can encode a second bit of position information of a to-be-encoded vertex. The second bit is a to-be-encoded symbol.

[0205] FIG. 16 illustrates an example flowchart of an encoding method 1600 for Trisoup geometry coding based on inter prediction according to some embodiments of the present application. In some embodiments, the encoder can be the execution subject of the encoding method. The encoder can be the G-PCC encoder 116, the geometry coding module 210, and the encoding module 503 as described in FIG. 1, FIG. 2, and FIG. 5. The encoder can encode a second bit in the position information of a vertex to be encoded. The second bit is the symbol to be encoded.

[0206] As shown in FIG. 16, in 1601, the encoder can determine a prediction mode based on a first syntax element and a compensation reference vertex information, wherein the first syntax element can be configured to indicate whether inter prediction is enabled, and the compensation reference vertex information can be configured to indicate whether there is a compensation reference vertex of the vertex to be encoded after the inter prediction mode is enabled. In some embodiments, when the first syntax element indicates that inter prediction is not enabled or the compensation reference vertex information can be configured to indicate that there is no compensation reference vertex of the vertex to be encoded after the inter prediction mode is enabled, the encoder can determine that the prediction mode is an intra prediction mode, i.e., the encoding method flow can proceed to 1602. In some embodiments, based on the first syntax element indicating that inter prediction is enabled and the compensation reference vertex information can be configured to indicate that there is a compensation reference vertex of the vertex to be encoded after the inter prediction mode is enabled, the encoder can determine that the prediction mode is an inter prediction mode. That is, the encoding method flow can proceed to 1603.

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

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

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

[0210] At 1605, the encoder can determine a prediction value of the to-be-encoded symbol. In some embodiments, the encoder determines the prediction value based on a second syntax element and a same bit information of uncompensated reference vertex information of the to-be-encoded vertex, where a value of the second syntax element is determined based on a prediction result of a neighbor uncompensated reference vertex predicting a neighbor vertex second bit, the uncompensated reference vertex information, and a prediction result of an uncompensated reference vertex first bit. In some embodiments, the value of the second syntax element is not equal to 1 based on that the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit does not satisfy a preset condition or the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist or the prediction result of the uncompensated reference vertex first bit does not satisfy the preset condition; the value of the second syntax element is equal to 1 based on that the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit satisfies the preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the uncompensated reference vertex first bit satisfies the preset condition. In some embodiments, the preset condition refers to that the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit or the prediction result of the uncompensated reference vertex first bit is good, i.e., the prediction accuracy of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit or the uncompensated reference vertex first bit is high.

[0211] In some embodiments, based on that the value of the second syntax element is not equal to 1, the encoder can determine that the to-be-encoded symbol is not predicted based on the uncompensated reference vertex information, i.e., NoPred. In some embodiments, based on that the value of the second syntax element is equal to 1, the encoder can determine the prediction value of the to-be-encoded symbol based on the same bit information of the uncompensated reference vertex information of the to-be-encoded vertex. Based on that the same bit information of the uncompensated reference vertex information of the to-be-encoded vertex is 0, the encoder can determine that the prediction value of the to-be-encoded symbol is 0, i.e., Pred 0. Based on that the same bit information of the uncompensated reference vertex information of the to-be-encoded vertex is 1, the encoder can determine that the prediction value of the to-be-encoded symbol is 1, i.e., Pred 1.

[0212] In some embodiments, based on the prediction value of the to-be-encoded symbol being 0 or 1, the encoder can update the prediction value of the to-be-encoded symbol based on the first quantity information and the second quantity information, wherein the first quantity information is the accumulated sum of the second bits determined to be predicted in the position information of the encoded vertex, and the second quantity information is the accumulated sum of the second bits in the position information of the encoded vertex. In some embodiments, the encoder can update the prediction value of the to-be-encoded symbol 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 being not greater than the preset threshold, the encoder can update the prediction value of the to-be-encoded symbol to be not predicted; that is, when the prediction value is 0 or 1, based on the ratio of the first quantity information and the second quantity information being not greater than the preset threshold, the prediction value is updated to be not predicted. 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 prediction value; that is, when the prediction 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 prediction value remains the original prediction value 0 or 1. In some embodiments, the prediction value determined by the encoder can include the following cases: 1. not predicted; 2. the prediction value before updating is 0, and the prediction value after updating is not predicted; 3. the prediction value before updating is 1, and the prediction value after updating is not predicted; 4. the prediction value is 0; 5. the prediction value is 1.

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

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

[0215] In some embodiments, the encoder can include one or more context probability model groups 1 - context probability model group H, where H = 7. In some embodiments, H can also be determined based on the number of bits of the position information in the vertex to be encoded. Wherein the context probability model group 1 corresponds to the intra context state tree; the context probability model group 2 corresponds to the inter context state tree one and the prediction value is no prediction; the context probability model group 3 corresponds to the inter context state tree one and the prediction value is 0; the context probability model group 4 corresponds to the inter context state tree one and the prediction value is 1; the context probability model group 5 corresponds to the inter context state tree two and the prediction value is no prediction; the context probability model group 6 corresponds to the inter context state tree two and the prediction value is 0; and the context probability model group 7 corresponds to the inter context state tree two and the prediction value is 1.

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

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

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

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

[0220] In some embodiments, the encoding method described in the present application can merge part of the inter-frame context state tree, thereby avoiding the problem that the probability corresponding to the inter-frame context state tree converges slowly when the use of inter-frame prediction is less, thereby having no good prediction effect. In addition, the encoding method described in the present application can also avoid that different prediction values share the same context probability model group, thereby improving the efficiency of the probability update of the context probability model group.

[0221] FIG. 17 shows an exemplary flowchart of a decoding method 1700 of Trisoup geometry decoding based on inter-frame prediction in some embodiments of the present application. In some embodiments, the execution subject of the decoding method can be a decoder. The decoder can decode the second bit in the position information of the to-be-decoded vertex. The second bit is the to-be-decoded symbol.

[0222] As shown in FIG. 17, in 1701, the decoder can determine a prediction mode based on a first syntax element and compensation reference vertex information, wherein 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 there is a compensation reference vertex of the to-be-decoded vertex after the inter-frame prediction mode is enabled. In some embodiments, when the first syntax element indicates that inter-frame prediction is not enabled or the compensation reference vertex information can be configured to indicate that there is no compensation reference vertex of the to-be-decoded vertex after the inter-frame prediction mode is enabled, the decoder can determine that the prediction mode is an intra-frame prediction mode, i.e., the decoding method flow can proceed to 1702. In some embodiments, based on the first syntax element indicating that inter-frame prediction is enabled and the compensation reference vertex information can be configured to indicate that there is a compensation reference vertex of the to-be-decoded vertex after the inter-frame prediction mode is enabled, the decoder can determine that the prediction mode is an inter-frame prediction mode. That is, the decoding method flow can proceed to 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 context state tree based on the parity bit information of the compensated reference vertex information. In some embodiments, when the decoder determines that the prediction mode is the inter prediction mode, the decoder can determine the inter context state tree according to the parity bit information of the compensated reference vertex information. In some embodiments, the inter context state tree can be divided into an inter context state tree one and an inter context state tree two. In some embodiments, when the parity bit information of the compensated reference vertex information is 0, the decoder can determine that the inter context state tree is the inter context state tree one. When the parity bit information of the compensated reference vertex information is 1, the decoder can determine that the inter context state tree is the inter context state tree two.

[0226] In 1705, the decoder can determine the prediction value of the to-be-decoded symbol. In some embodiments, the decoder determines the prediction value based on a second syntax element and the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex, wherein the value of the second syntax element is determined based on a prediction result of a neighbor uncompensated reference vertex predicting a neighbor vertex second bit, the uncompensated reference vertex information, and a prediction result of an uncompensated reference vertex first bit. In some embodiments, when the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit does not satisfy a preset condition or the uncompensated reference vertex information indicates that the uncompensated reference vertex does not exist or the prediction result of the uncompensated reference vertex first bit does not satisfy the preset condition, the value of the second syntax element is not equal to 1; when the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit satisfies the preset condition, the uncompensated reference vertex information indicates that the uncompensated reference vertex exists, and the prediction result of the uncompensated reference vertex first bit satisfies the preset condition, the value of the second syntax element is equal to 1. In some embodiments, the preset condition refers to that the prediction result of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit or the prediction result of the uncompensated reference vertex first bit is good, i.e., the prediction accuracy of the neighbor uncompensated reference vertex predicting the neighbor vertex second bit or the uncompensated reference vertex first bit is high.

[0227] In some embodiments, based on the value of the second syntax element not equaling 1, the decoder can determine that the to-be-decoded symbol is not predicted using uncompensated reference vertex information, i.e., NoPred. In some embodiments, based on the value of the second syntax element equaling 1, the decoder can determine the predicted value of the to-be-decoded symbol based on the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex. Based on the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex being 0, the decoder can determine that the predicted value of the to-be-decoded symbol is 0 (Pred 0). Based on the parity bit information of the uncompensated reference vertex information of the to-be-encoded vertex being 1, the decoder can determine that the predicted value of the to-be-decoded symbol is 1 (Pred 1).

[0228] In some embodiments, based on the predicted value of the to-be-decoded symbol being 0 or 1, the decoder can update the predicted value of the to-be-decoded symbol based on sixth quantity information and seventh quantity information, where the sixth quantity information is the cumulative sum of the second bit in the position information of the decoded vertex determined to be predicted, and the seventh quantity information is the cumulative sum of the second bit in the position information of the decoded vertex. In some embodiments, the decoder can update the predicted value of the to-be-decoded symbol 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 to-be-decoded symbol to be NoPred; i.e., 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 be NoPred. 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; i.e., 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 can include the following cases: 1. NoPred; 2. the predicted value before updating is 0, and the predicted value after updating is NoPred; 3. the predicted value before updating is 1, and the predicted value after updating is NoPred; 4. the predicted value is 0; 5. the 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 to-be-decoded symbol. In some embodiments, when the prediction mode is an intra prediction mode, the decoder determines, based on an intra context state tree, that a context probability model can be configured to decode the to-be-decoded symbol.

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

[0231] In some embodiments, the decoder can include one or more context probability model groups 1 - context probability model group H, where H = 7. In some embodiments, H can also be determined based on the number of bits of the position information in the vertex to be decoded. Wherein the context probability model group 1 corresponds to the intra context state tree; the context probability model group 2 corresponds to the inter context state tree one and the prediction value is no prediction; the context probability model group 3 corresponds to the inter context state tree one and the prediction value is 0; the context probability model group 4 corresponds to the inter context state tree one and the prediction value is 1; the context probability model group 5 corresponds to the inter context state tree two and the prediction value is no prediction; the context probability model group 6 corresponds to the inter context state tree two and the prediction value is 0; and the context probability model group 7 corresponds to the inter context state tree two and the prediction value is 1.

[0232] In some embodiments, the decoder can include one or more context probability model groups 1 - context probability model group H, where H = 7. In some embodiments, H can also be determined based on the number of bits of the position information in the vertex to be decoded. In some embodiments, the prediction value determined by the decoder can include the following cases: 1. no prediction; 2. prediction value before update is 0, and prediction value after update is no prediction; 3. prediction value before update is 1, and prediction value after update is no prediction; 4. prediction value is 0; 5. prediction value is 1. Wherein, based on the context state tree being an intra context state tree, the decoder adopts context probability model group 1; based on the context state tree being an inter context state tree one and the prediction value being no prediction, the decoder adopts context probability model group 2; based on the context state tree being an inter context state tree one and the prediction value being prediction value before update is 0, and prediction value after update is no prediction, the decoder adopts context probability model group 3; based on the context state tree being an inter context state tree one and the prediction value being prediction value before update is 1, and prediction value after update is no prediction, the decoder adopts context probability model group 4; based on the context state tree being an inter context state tree one and the prediction value being 0, the decoder adopts context probability model group 3; based on the context state tree being an inter context state tree one and the prediction value being 1, the decoder adopts context probability model group 4; based on the context state tree being an inter context state tree two and the prediction value being no prediction, the decoder adopts context probability model group 5; based on the context state tree being an inter context state tree two and the prediction value being prediction value before update is 0, and prediction value after update is no prediction, the decoder adopts context probability model group 6; based on the context state tree being an inter context state tree two and the prediction value being prediction value before update is 1, and prediction value after update is no prediction, the decoder adopts context probability model group 7; based on the context state tree being an inter context state tree two and the prediction value being 0, the decoder adopts context probability model group 6; based on the context state tree being an inter context state tree two and the prediction value being 1, the decoder adopts context probability model group 7.

[0233] In some embodiments, each of the context probability model groups can include one or more context probability models. In some embodiments, after determining the corresponding context probability model group, the context probability model group determines a corresponding context probability model index based on the determined context state tree, where the context probability model index can 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 to-be-decoded symbol based on the context probability model.

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

[0236] In some embodiments, some embodiments of the present application further provide a non-volatile storage medium which can be configured to store computer readable instructions for implementing the encoding method and / or the decoding method provided by the embodiments of the present application. When the computer readable instructions are called by a processor, the processor can be configured to execute the encoding method and / or the decoding method provided by the embodiments of the present application.

[0237] In some embodiments, some embodiments of the present application further provide a chip which can include a memory configured to store computer readable instructions executable for implementing the encoding method and / or the decoding method provided by the embodiments of the present application. In the embodiments of the present application, the chip can include a processor. When the computer readable instructions are called by the processor, the processor can be configured to execute and / or implement the encoding method and / or the decoding method provided by the embodiments of the present application.

[0238] It should be noted that the various modules provided by some embodiments of the present application can be implemented wholly or partially by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor and / or the controller of the hardware structure in the form of hardware, or stored in the memory of the hardware structure in the form of software, so as to be called and executed by the processor and / or the controller to execute the operations corresponding to the above modules.

[0239] For the sake of simplicity, the methods of the present application are depicted and described as a series of acts. However, the acts of the present application can occur in various orders and / or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts can be required to implement a method in accordance with the present application. In addition, those skilled in the art will understand and appreciate the context of the acts of the present application, and recognize that the acts represented could be implemented by state machines or as events, among other possibilities.

[0240] The present application has been described in terms of specific embodiments thereof. It will be apparent, however, to those skilled in the art that many modifications, changes and substitutions can be made thereto without departing from the application. It is intended to embrace all such modifications, changes and substitutions within the scope of the present application.

[0241] The terms "first", "second", "third", "fourth" and the like as used herein are labels that can be configured to distinguish between different elements, and can not necessarily have an ordinal meaning.

[0242] While the forgoing is a complete description of the application, various modifications, alternative constructions, and equivalents can employed. Accordingly, the application is not limited to the specific embodiments that have been described herein.

Claims

A coding method based on inter-frame prediction includes: 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. According to the method of 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. According to the method of 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. 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. 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. According to the method of 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. According to the method of 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. The method according to any one of claims 1-20, wherein, The symbol to be encoded is the first bit in the location information. An encoder based on inter-frame prediction includes: 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. The encoder according to claim 22, wherein, The symbol to be encoded is the first bit in the location information. A decoding method based on inter-frame prediction includes: 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. The method according to 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. 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. 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. 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. 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. 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. 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. The method according to any one of claims 24-40, wherein, The symbol to be decoded is the first bit in the location information. A decoder based on inter-frame prediction includes: 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. The decoder according to claim 45, wherein, The symbol to be decoded is the first bit in the location information. A coding method based on inter-frame prediction includes: 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. The method according to claim 47, wherein, The symbol to be encoded indicates the position information of the vertex to be encoded, the position The 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. 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. 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. 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. 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. 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. 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. 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. 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. The method according to 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. 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. 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. 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. According to the method of claim 60, 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. The method according to claim 61, 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, 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. According to the method of claim 58, 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. 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. 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. 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. The method according to any one of claims 47-63, wherein, The symbol to be encoded is the second bit in the location information. An encoder based on inter-frame prediction includes: 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. The encoder according to claim 68, wherein, The symbol to be encoded is the second bit in the location information. A decoding method based on inter-frame prediction includes: 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. According to the method of claim 83, 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. The method according to claim 84, 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, 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. According to the method of claim 81, 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. 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. 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. 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. The method according to any one of claims 70-86, wherein, The symbol to be decoded is the second bit in the location information. A decoder based on inter-frame prediction includes: 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. The decoder according to claim 91, wherein, The symbol to be decoded is the second bit in the location information. 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. 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. 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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