Coding and decoding method, encoder, decoder, code stream and storage medium

Abstract

The embodiment of the invention provides an encoding and decoding method, an encoder, a decoder, a code stream and a storage medium, the decoding method is applied to the decoder, and the decoding method comprises the following steps: searching a first co-located node of a current node in a first reference image according to geometric information of the current node of the current image, searching a second co-location node of the current node in a second reference image; and performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

Description

Coding and decoding method, encoder, decoder, code stream and storage medium Technical Field

[0001] The embodiments of the present application relate to the field of point cloud compression technology, and in particular to a coding and decoding method, an encoder, a decoder, a code stream, and a storage medium. Background Art

[0002] In the geometry-based Point Cloud Compression (G-PCC) codec framework or video-based Point Cloud Compression (V-PCC) codec framework provided by the Moving Picture Experts Group (MPEG), the geometric information and attribute information of the point cloud are encoded separately.

[0003] At present, attribute information encoding mainly targets the encoding of color information. In color information encoding, there are two main transformation methods: one is the distance-based lifting transformation that relies on the level of detail (LOD) division, and the other is the direct region adaptive hierarchical transform (RAHT).

[0004] However, in the related schemes of attribute RAHT inter-frame prediction coding, there is still a need to further improve the accuracy of inter-frame attribute prediction.

[0005] Summary of the Invention

[0006] The embodiments of the present application provide a coding and decoding method, an encoder, a decoder, a code stream, and a storage medium, which can improve the accuracy of inter-frame attribute prediction, thereby further compressing the temporal redundancy between images and saving point cloud code streams.

[0007] The technical solution of the embodiment of the present application can be implemented as follows:

[0008] In a first aspect, an embodiment of the present application provides a decoding method, applied to a decoder, the method comprising: searching for a first co-located node of the current node in a first reference image and searching for a second co-located node of the current node in a second reference image based on geometric information of the current node of the current image; performing inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0009] In second aspect, an embodiment of the present application provides an encoding method, applied to an encoder, the method comprising: searching for a first co-located node of the current node in a first reference image and searching for a second co-located node of the current node in a second reference image based on geometric information of the current node of the current image; performing inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0010] In a third aspect, an embodiment of the present application provides a decoder comprising: a first search module, configured to search for a first co-located node of the current node in a first reference image and a second co-located node of the current node in a second reference image based on geometric information of the current node of the current image; a first prediction module, configured to perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0011] In a fourth aspect, an embodiment of the present application provides a decoder, comprising a first memory and a first processor; wherein,

[0012] The first memory is used to store a computer program that can be run on the first processor; the first processor is used to execute the decoding method as described in the embodiment of the present application when running the computer program.

[0013] In the fifth aspect, an embodiment of the present application provides an encoder, which includes: a second search module, configured to search for a first co-located node of the current node in a first reference image and a second co-located node of the current node in a second reference image based on geometric information of the current node of the current image; a second prediction module, configured to perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0014] In a sixth aspect, an embodiment of the present application provides an encoder, comprising a second memory and a second processor; wherein,

[0015] The second memory is used to store a computer program that can be run on the second processor; the second processor is used to execute the encoding method as described in the embodiment of the present application when running the computer program.

[0016] In a seventh aspect, an embodiment of the present application provides a code stream, which is obtained using the encoding method described in the embodiment of the present application.

[0017] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the encoding method described in the embodiment of the present application, or implements the decoding method described in the embodiment of the present application.

[0018] The embodiments of the present application provide a coding and decoding method, an encoder, a decoder, a code stream, and a storage medium. For example, in the coding method, when determining the attribute prediction value of the current node, not only the current node's co-located node in the first reference image is used, but also the current node's co-located node in the second reference image is used; this is beneficial to improving the accuracy of inter-frame attribute prediction, thereby further compressing the temporal redundancy between images and saving point cloud code streams. For example, in the decoding method, an inter-frame attribute prediction method similar to that used at the encoding end is adopted, that is, when determining the attribute prediction value of the current node, not only the current node's co-located node in the first reference image is used, but also the current node's co-located node in the second reference image is used; this is beneficial to improving the accuracy of inter-frame attribute prediction, thereby facilitating the recovery of higher-quality point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG1A is a schematic diagram of a three-dimensional point cloud image;

[0020] FIG1B is a partially enlarged view of a three-dimensional point cloud image;

[0021] FIG2A is a schematic diagram of six viewing angles of a point cloud image;

[0022] FIG2B is a schematic diagram of a data storage format corresponding to a point cloud image;

[0023] FIG3 is a schematic diagram of a network architecture for point cloud encoding and decoding;

[0024] FIG4A is a schematic diagram of a composition framework of a G-PCC encoder;

[0025] FIG4B is a schematic diagram of a composition framework of a G-PCC decoder;

[0026] FIG5A is a schematic diagram of a low plane position in the Z-axis direction;

[0027] FIG5B is a schematic diagram of a high plane position in the Z-axis direction;

[0028] FIG6 is a schematic diagram of a node coding sequence;

[0029] FIG7A is a schematic diagram of plane identification information;

[0030] FIG7B is a schematic diagram of another type of planar identification information;

[0031] FIG8 is a schematic diagram of sibling nodes of a current node;

[0032] Figure 9 is a schematic diagram of the intersection of a laser radar and a node;

[0033] FIG10 is a schematic diagram of neighborhood nodes at the same partition depth and the same coordinates;

[0034] FIG11 is a schematic diagram of a current node being located at a low plane position of a parent node;

[0035] FIG12 is a schematic diagram showing a current node being located at a high plane position of a parent node;

[0036] FIG13 is a schematic diagram of predictive coding of planar position information of a laser radar point cloud;

[0037] FIG14 is a schematic diagram of IDCM encoding;

[0038] FIG15 is a schematic diagram of coordinate transformation of a rotating laser radar to obtain a point cloud;

[0039] FIG16 is a schematic diagram of predictive coding in the X-axis or Y-axis direction;

[0040] FIG17A is a schematic diagram showing an angle of the Y plane predicted by the horizontal azimuth angle;

[0041] FIG17B is a schematic diagram showing an angle of the X-plane predicted by the horizontal azimuth angle;

[0042] FIG18 is another schematic diagram of predictive coding in the X-axis or Y-axis direction;

[0043] FIG19A is a schematic diagram of three intersection points included in a sub-block;

[0044] FIG19B is a schematic diagram of a triangular facet set fitted using three intersection points;

[0045] FIG19C is a schematic diagram of upsampling of a triangle face set;

[0046] FIG20 is a schematic diagram of a distance-based LOD construction process;

[0047] FIG21 is a schematic diagram of a visualization result of an LOD generation process;

[0048] FIG22 is a schematic diagram of an encoding process for attribute prediction;

[0049] FIG23 is a schematic diagram of the composition of a pyramid structure;

[0050] FIG24 is a schematic diagram showing the composition of another pyramid structure;

[0051] FIG25 is a schematic diagram of an LOD structure for inter-layer nearest neighbor search;

[0052] FIG26 is a schematic diagram of a nearest neighbor search structure based on spatial relationships;

[0053] FIG27A is a schematic diagram of a coplanar spatial relationship;

[0054] FIG27B is a schematic diagram of a coplanar and colinear spatial relationship;

[0055] FIG27C is a schematic diagram of a spatial relationship of coplanarity, colinearity, and copointness;

[0056] FIG28 is a schematic diagram of inter-layer prediction based on fast search;

[0057] FIG29 is a schematic diagram of an LOD structure for nearest neighbor search within an attribute layer;

[0058] FIG30 is a schematic diagram of intra-layer prediction based on fast search;

[0059] FIG31 is a block-based neighborhood search structure diagram;

[0060] FIG32 is a schematic diagram of an encoding process of a lifting transform;

[0061] FIG33 is a schematic diagram of a RAHT attribute transformation coding structure;

[0062] FIG34 is a schematic diagram of a RAHT transformation process along the x, y, and z directions;

[0063] FIG35A is a schematic diagram of a RAHT forward transformation process;

[0064] FIG35B is a schematic diagram of a RAHT inverse transformation process;

[0065] FIG36 is a schematic diagram of the structure of an attribute coding block;

[0066] FIG37 is a schematic diagram of the overall process of RAHT attribute prediction transform coding;

[0067] FIG38 is a schematic diagram of a neighborhood prediction relationship of a current block;

[0068] FIG39 is a schematic diagram of a calculation process of an attribute transformation coefficient;

[0069] FIG40 is a schematic diagram of a RAHT attribute inter-frame prediction coding structure;

[0070] FIG41 is a schematic diagram of a flow chart of an encoding method provided in an embodiment of the present application;

[0071] FIG42 is a schematic diagram showing the principle of bidirectional inter-frame attribute prediction provided by an embodiment of the present application;

[0072] FIG43 is a schematic diagram of a specific implementation flow of step 412 provided in an embodiment of the present application;

[0073] FIG44 is a schematic diagram of an implementation flow of a decoding method provided in an embodiment of the present application;

[0074] FIG45 is a schematic diagram of a RAHT coding layer provided in an embodiment of the present application;

[0075] FIG46 is a schematic diagram of the structure of a decoder provided in an embodiment of the present application;

[0076] FIG47 is a schematic diagram of the structure of an encoder provided in an embodiment of the present application;

[0077] FIG48 is a schematic diagram of the structure of a decoder provided in an embodiment of the present application;

[0078] Figure 49 is a schematic diagram of the structure of the encoder provided in an embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to enable a more detailed understanding of the features and technical contents of the embodiments of the present application, the implementation of the embodiments of the present application is described in detail below with reference to the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present application.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0081] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0082] It should also be pointed out that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0083] Point cloud is a three-dimensional representation of the surface of an object. Point cloud (data) of the surface of an object can be collected through acquisition equipment such as photoelectric radar, lidar, laser scanner, and multi-view camera.

[0084] A point cloud is a set of irregularly distributed discrete points in space that express the spatial structure and surface properties of a three-dimensional object or scene. Figure 1A shows a three-dimensional point cloud image and Figure 1B shows a partially enlarged view of the three-dimensional point cloud image. It can be seen that the point cloud surface is composed of densely distributed points.

[0085] Two-dimensional images contain information at every pixel, and their distribution is regular, so there's no need to record their location information. However, the distribution of points in a point cloud in three-dimensional space is random and irregular, so recording the location of each point in space is necessary to fully represent a point cloud. Similar to two-dimensional images, each location in the acquisition process has corresponding attribute information, typically an RGB color value, which reflects the object's color. For point clouds, in addition to color information, each point's corresponding attribute information often includes reflectance values, which reflect the surface texture of the object. Therefore, point cloud data typically includes geometric information consisting of three-dimensional position information, three-dimensional color information, and attribute information consisting of one-dimensional reflectance information. Points in a point cloud can include both position information and attribute information. For example, the position information of a point can be its three-dimensional coordinate information (x, y, z). The position information of a point can also be referred to as its geometric information. For example, the attribute information of a point can include color information (three-dimensional color information) and / or reflectance (one-dimensional reflectance information r). For example, the color information can be information in any color space, such as RGB information. Here, R represents red (Red, R), G represents green (Green, G), and B represents blue (Blue, B). For another example, the color information can be luminance and chrominance (YCbCr, YUV) information. Here, Y represents brightness (Luma), Cb (U) represents the blue color difference, and Cr (V) represents the red color difference.

[0086] For example, a point cloud generated using laser measurement principles can include both its 3D coordinate information and its reflectivity. For another example, a point cloud generated using photogrammetry principles can include both its 3D coordinate information and its 3D color information. For another example, a point cloud generated using a combination of laser measurement and photogrammetry principles can include both its 3D coordinate information, its reflectivity value, and its 3D color information.

[0087] Figures 2A and 2B show a point cloud image and its corresponding data storage format. Figure 2A provides six viewing angles of the point cloud image, while Figure 2B consists of a file header and data. The header includes the data format, data representation type, the total number of points in the point cloud, and the content represented by the point cloud. For example, the point cloud is in ".ply" format, represented by ASCII code, with a total of 207,242 points. Each point has 3D coordinate information (x, y, z) and 3D color information (r, g, b).

[0088] Point clouds can be divided into the following categories according to the acquisition method:

[0089] Static point cloud: the object is stationary and the device that acquires the point cloud is also stationary;

[0090] Dynamic point cloud: The object is moving, but the device that obtains the point cloud is stationary;

[0091] Dynamic point cloud acquisition: The device used to acquire the point cloud is in motion.

[0092] For example, point clouds can be divided into two categories according to their usage:

[0093] Category 1: Machine perception point cloud, which can be used in scenarios such as autonomous navigation systems, real-time inspection systems, geographic information systems, visual sorting robots, and disaster relief robots;

[0094] Category 2: Human eye perception point cloud, which can be used in point cloud application scenarios such as digital cultural heritage, free viewpoint broadcasting, 3D immersive communication, and 3D immersive interaction.

[0095] Point clouds can flexibly and conveniently express the spatial structure and surface properties of three-dimensional objects or scenes. Moreover, since point clouds are obtained by directly sampling real objects, they can provide a strong sense of reality while ensuring accuracy. Therefore, they are widely used, including virtual reality games, computer-aided design, geographic information systems, automatic navigation systems, digital cultural heritage, free viewpoint broadcasting, three-dimensional immersive remote presentation, and three-dimensional reconstruction of biological tissues and organs.

[0096] Point clouds are primarily collected through computer generation, 3D laser scanning, and 3D photogrammetry. Computers can generate point clouds of virtual 3D objects and scenes; 3D laser scanning can obtain point clouds of static real-world 3D objects or scenes, generating millions of point clouds per second; and 3D photogrammetry can obtain point clouds of dynamic real-world 3D objects or scenes, generating tens of millions of point clouds per second. These technologies reduce the cost and time required to acquire point cloud data while improving data accuracy. While changes in point cloud data acquisition methods have made it possible to acquire large amounts of point cloud data, the processing of this massive amount of 3D point cloud data is facing bottlenecks due to storage space and transmission bandwidth constraints, as application demands grow.

[0097] For example, taking a point cloud video with a frame rate of 30 frames per second (fps), each frame contains 700,000 points, and each point has coordinate information (xyz, float) and color information (RGB, uchar). Therefore, the data volume of a 10-second point cloud video is approximately 0.7 million × (4 bytes × 3 + 1 byte × 3) × 30 fps × 10 seconds = 3.15 GB, where 1 byte is 10 bits. For a 1280 × 720 2D video with a YUV sampling format of 4:2:0 and a frame rate of 24 fps, the data volume for 10 seconds is approximately 1280 × 720 × 12 bits × 24 fps × 10 seconds ≈ 0.33 GB. The data volume of a 10-second two-view 3D video is approximately 0.33 × 2 = 0.66 GB. This shows that the data volume of a point cloud video far exceeds that of 2D and 3D videos of the same length. Therefore, in order to better realize data management, save server storage space, and reduce the transmission traffic and transmission time between the server and the client, point cloud compression has become a key issue in promoting the development of the point cloud industry.

[0098] That is to say, since the point cloud is a collection of massive points, storing the point cloud not only consumes a lot of memory, but is also not conducive to transmission. There is also not enough bandwidth to support direct transmission of the point cloud at the network layer without compression. Therefore, the point cloud needs to be compressed.

[0099] Currently, the point cloud coding framework that can compress point clouds can be the geometry-based Point Cloud Compression (G-PCC) codec framework or the video-based Point Cloud Compression (V-PCC) codec framework provided by the Moving Picture Experts Group (MPEG), or the AVS-PCC codec framework provided by AVS. The G-PCC codec framework can be used to compress the first type of static point clouds and the third type of dynamically acquired point clouds, which can be based on the Point Cloud Compression Test Platform (Test Model Compression 13, TMC13). The V-PCC codec framework can be used to compress the second type of dynamic point clouds, which can be based on the Point Cloud Compression Test Platform (Test Model Compression 2, TMC2). Therefore, the G-PCC codec framework is also called the point cloud codec TMC13, and the V-PCC codec framework is also called the point cloud codec TMC2.

[0100] An embodiment of the present application provides a network architecture of a point cloud encoding and decoding system including a decoding method and an encoding method. FIG3 is a schematic diagram of a network architecture of a point cloud encoding and decoding system provided by an embodiment of the present application. As shown in FIG3 , the network architecture includes one or more electronic devices 13 to 1N and a communication network 01, wherein the electronic devices 13 to 1N can perform video interaction through the communication network 01. During the implementation process, the electronic device can be various types of devices with point cloud encoding and decoding functions. For example, the electronic device can include a mobile phone, a tablet computer, a personal computer, a personal digital assistant, a navigator, a digital phone, a video phone, a television, a sensor device, a server, etc., which is not limited by the embodiment of the present application. Among them, the decoder or encoder in the embodiment of the present application can be the above-mentioned electronic device.

[0101] Among them, the electronic device in the embodiment of the present application has a point cloud encoding and decoding function, generally including a point cloud encoder (ie, encoder) and a point cloud decoder (ie, decoder).

[0102] The following describes the related technologies using the G-PCC encoding and decoding framework as an example.

[0103] As you can understand, in the point cloud G-PCC codec framework, the point cloud data to be encoded is first divided into multiple slices through slice partitioning. In each slice, the geometric information of the point cloud and the attribute information corresponding to each point are encoded separately.

[0104] Figure 4A shows a schematic diagram of the G-PCC encoder architecture. As shown in Figure 4A, during the geometry encoding process, the geometric information is transformed so that the entire point cloud is contained within a bounding box. Quantization is then performed. This quantization step primarily serves a scaling purpose. Due to quantization rounding, the geometric information of some point clouds becomes identical. Parameters are then used to determine whether to remove duplicate points. This process of quantization and removing duplicate points is also known as voxelization. The bounding box is then partitioned into an octree or a prediction tree is constructed. During this process, arithmetic coding is performed on the points in the leaf nodes of the partition to generate a binary geometry bitstream. Alternatively, arithmetic coding is performed on the intersection points (vertices) generated by the partition (surface fitting is performed based on the intersection points) to generate a binary geometry bitstream. During the attribute encoding process, after the geometry encoding is completed and the geometric information is reconstructed, color conversion is performed to convert the color information (i.e., attribute information) from the RGB color space to the YUV color space. The reconstructed geometry information is then used to recolor the point cloud so that the unencoded attribute information corresponds to the reconstructed geometry information. Attribute encoding is mainly performed on color information. During the color information encoding process, there are two main transformation methods. One is the distance-based lifting transform that relies on the level of detail (LOD) division, and the other is the direct region adaptive hierarchical transform (RAHT). Both methods convert color information from the spatial domain to the frequency domain, obtain high-frequency coefficients and low-frequency coefficients through the transformation, and finally quantize the coefficients. Then, the quantized coefficients are arithmetically encoded to generate a binary attribute bitstream.

[0105] Figure 4B shows a schematic diagram of the composition framework of a G-PCC decoder. As shown in Figure 4B, for the acquired binary bit stream, the geometric bit stream and attribute bit stream in the binary bit stream are first decoded independently. When decoding the geometric bit stream, the geometric information of the point cloud is obtained through arithmetic decoding-reconstruction of the octree / reconstruction of the prediction tree-reconstruction of the geometry-coordinate inverse conversion; when decoding the attribute bit stream, the attribute information of the point cloud is obtained through arithmetic decoding-inverse quantization-LOD partitioning / RAHT-color inverse conversion, and the point cloud data to be encoded (i.e., the output point cloud) is restored based on the geometric information and attribute information.

[0106] It should be noted that, as shown in FIG4A or FIG4B , the current geometric coding and decoding of G-PCC can be divided into octree-based geometric coding and decoding (marked by a dotted box) and prediction tree-based geometric coding and decoding (marked by a dotted box).

[0107] For Octree geometry encoding (OctGeomEnc), the octree-based geometry encoding includes: first, coordinate conversion of the geometric information so that all point clouds are contained in a Bounding Box. Then quantization is performed. This step of quantization mainly plays a role of scaling. Due to the quantization rounding, the geometric information of some points is the same. The parameters are used to decide whether to remove duplicate points. The process of quantization and removal of duplicate points is also called voxelization. Next, the Bounding Box is continuously divided into trees (such as octrees, quadtrees, binary trees, etc.) in the order of breadth-first traversal, and the placeholder code of each node is encoded. In related technologies, a company proposed an implicit geometric division method. First, the bounding box of the point cloud is calculated. Assume d x >d y >d z , the bounding box corresponds to a cuboid. When geometrically partitioning, the binary tree partitioning will be performed based on the x-axis to obtain two child nodes; until d is satisfied x =d y >d z When the conditions are met, the quadtree partitioning will be performed based on the x and y axes to obtain four child nodes; when d is finally satisfied x =d y =d z When the conditions are met, the octree partitioning will continue until the leaf node obtained by the partitioning is a 1×1×1 unit cube. The partitioning will stop, and the points in the leaf node will be encoded to generate a binary code stream. In the process of binary tree / quadtree / octree partitioning, two parameters are introduced: K and M. Parameter K indicates the maximum number of binary tree / quadtree partitions before octree partitioning; parameter M is used to indicate that the minimum block side length corresponding to binary tree / quadtree partitioning is 2M. At the same time, K and M must meet the conditions: Assume d max =max(d x ,d y ,d z ), d min =min(d x ,d y ,d z ), parameter K satisfies: K ≥ d max -d min ; Parameter M satisfies: M≥d minThe reason why the parameters K and M meet the above conditions is that in the current G-PCC geometric implicit partitioning process, the priority of the partitioning method is binary tree, quadtree and octree. When the node block size does not meet the conditions of binary tree / quadtree, the node will be divided into octree until the minimum unit of leaf node is 1×1×1. The octree-based geometric information coding mode can effectively encode the geometric information of the point cloud by utilizing the correlation between adjacent points in space. However, for some relatively flat nodes or nodes with planar characteristics, the coding efficiency of the point cloud geometric information can be further improved by using plane coding.

[0108] Exemplarily, Figure 5A and Figure 5B provide a kind of plane position schematic diagram.Wherein, Figure 5A shows a kind of low plane position schematic diagram in the Z-axis direction, and Figure 5B shows a kind of high plane position schematic diagram in the Z-axis direction.As shown in Figure 5A, here (a), (a0), (a1), (a2), (a3) ​​all belong to the low plane position in the Z-axis direction. Taking (a) as an example, it can be seen that the four child nodes occupied in the current node are all located at the low plane position of the current node in the Z-axis direction, so it can be considered that the current node belongs to a Z plane and is a low plane in the Z-axis direction. Similarly, as shown in Figure 5B, here (b), (b0), (b1), (b2), (b3) all belong to the high plane position in the Z-axis direction. Taking (b) as an example, it can be seen that the four child nodes occupied in the current node are located at the high plane position of the current node in the Z-axis direction, so it can be considered that the current node belongs to a Z plane and is a high plane in the Z-axis direction.

[0109] Taking (a) in Figure 5A as an example, the efficiency of octree encoding and plane encoding is compared. Figure 6 provides a schematic diagram of the node encoding sequence, that is, node encoding is performed in the order of 0, 1, 2, 3, 4, 5, 6, and 7 as shown in Figure 6. Here, if octree encoding is used for (a) in Figure 5A, the placeholder information of the current node is represented as: 11001100. However, if plane encoding is used, first, an identifier must be encoded to indicate that the current node is a plane in the Z-axis direction. Secondly, if the current node is a plane in the Z-axis direction, the plane position of the current node must also be represented. Finally, only the placeholder information of the lower plane node in the Z-axis direction needs to be encoded (i.e., the placeholder information of the four child nodes 0, 2, 4, and 6). Therefore, encoding the current node based on plane encoding only requires 6 bits, which can reduce the representation by 2 bits compared to the related art octree encoding. Based on this analysis, plane encoding has significantly higher coding efficiency than octree encoding. Therefore, for an occupied node, if a plane encoding method is used in a certain dimension, it is first necessary to represent the plane identification (planarMode) and plane position (PlanePos) information of the current node in that dimension, and then encode the occupancy information of the current node based on the plane information of the current node. For example, FIG7A shows a schematic diagram of plane identification information. As shown in FIG7A, there is a low plane in the Z-axis direction; correspondingly, the value of the plane identification information is true (true) or 1, that is, planarMode_ Z = true; the plane position information is the low plane (low), that is, PlanePosition_ Z =low. FIG7B shows another schematic diagram of plane identification information. As shown in FIG7B, here it is not a plane in the Z-axis direction; correspondingly, the value of the plane identification information is false or 0, that is, planarMode_ Z =false.

[0110] It should be noted that for PlaneMode_ i :0 means the current node is not a plane in the i-axis direction, 1 means the current node is a plane in the i-axis direction. If the current node is a plane in the i-axis direction, then for PlanePosition_ i : 0 means the current node is a plane in the i-axis direction and the plane position is low, 1 means the current node is a high plane in the i-axis direction. Here, i represents the coordinate dimension, which can be the X-axis direction, Y-axis direction, or Z-axis direction, so i = 0, 1, 2.

[0111] In the G-PCC standard, when determining whether a node meets the conditions for planar coding and when the node meets the conditions for planar coding, it is necessary to perform predictive coding on the planar identifier and planar position information of the node.

[0112] In the embodiments of the present application, there are three judgment conditions in the current G-PCC standard for determining whether a node meets the planar coding conditions, and the following will explain them in detail one by one.

[0113] 1. Judge according to the planar probability of the node in each dimension.

[0114] (1) Determine the local area density (local_node_density) of the current node;

[0115] (2) Determine the probability Prob(i) of the current node in each dimension.

[0116] When the local area density of the node is less than the threshold Th (for example, Th = 3), the planar probabilities Prob(i) of the current node in the three coordinate dimensions are compared with the thresholds Th0, Th1, and Th2, where Th0 < Th1 < Th2 (for example, Th0 = 0.6, Th1 = 0.77, Th2 = 0.88). Here, Eligible i (i = 0, 1, 2) represents whether planar coding is started in each dimension: Eligible i = Prob(i) >= threshold.

[0117] It should be noted that the threshold is adaptively changed. For example, when Prob(0) > Prob(1) > Prob(2), the settings of Eligible i are as follows: Eligible0 = Prob(0) >= Th0; Eligible1 = Prob(1) >= Th1; Eligible2 = Prob(2) >= Th2.

[0118] When Prob(1) > Prob(0) > Prob(2), the settings of Eligible i are as follows: Eligible0 = Prob(0) >= Th1; Eligible1 = Prob(1) >= Th0; Eligible2 = Prob(2) >= Th2.

[0119] Here, the update of Prob(i) is specifically as follows: Prob(i) new = (L × Prob(i) + δ(coded node)) / L + 1

[0120] Where L = 255; in addition, if the coded node is a plane, δ(coded node) is 1; otherwise, δ(coded node) is 0.

[0121] Here, the update of local_node_density is as follows: local_node_density new =local_node_density+4*numSiblings

[0122] Where local_node_density is initialized to 4, and numSiblings is the number of sibling nodes of the node. For example, FIG8 shows a schematic diagram of the sibling nodes of the current node. As shown in FIG8 , the current node is a node filled with slashes, and the nodes filled with grids are sibling nodes. Then, the number of sibling nodes of the current node is 5 (including the current node itself).

[0123] Second, determine whether the current layer nodes meet the plane coding requirements based on the point cloud density of the current layer.

[0124] The density of the current layer points is used to determine whether to perform planar coding on the nodes of the current layer. Assuming that the number of points in the current point cloud to be coded is pointCount, the number of points reconstructed by the inferred direct coding model (IDCM) coding is numPointCountRecon, and because the octree is coded based on the order of breadth-first traversal, the number of nodes to be coded in the current layer can be obtained as nodeCount. Then, the assumption to determine whether to start planar coding in the current layer is planarEligibleKOctreeDepth, specifically: planarEligibleK OctreeDepth = (pointCount-numPointCountRecon) <nodeCount×1.3。

[0125] Among them, if (pointCount-numPointCountRecon) is less than nodeCount×1.3, then planarEligibleK OctreeDepth is true; if (pointCount-numPointCountRecon) is not less than nodeCount×1.3, then planarEligibleKOctreeDepth is false. In this way, when planarEligibleKOctreeDepth is true, all nodes in the current layer are planar coded; otherwise, all nodes in the current layer are not planar coded and only octree coding is used.

[0126] 3. Determine whether the current node meets the plane coding requirements based on the acquisition parameters of the lidar point cloud.

[0127] Figure 9 shows a schematic diagram of the intersection of a laser radar and a node. As shown in Figure 9, a node filled with a grid is simultaneously traversed by two laser beams, so the current node is not a plane in the direction perpendicular to the Z axis. A node filled with a diagonal line is small enough to be traversed by two laser beams simultaneously, so it is possible that the node filled with a diagonal line is a plane in the direction perpendicular to the Z axis.

[0128] Furthermore, for nodes that meet the plane coding conditions, predictive coding may be performed on the plane identification information and the plane position information.

[0129] First, predictive coding of plane identification information.

[0130] Here, only three context information are used for encoding, that is, the plane identification in each coordinate dimension is designed separately for context.

[0131] Secondly, predictive coding of plane position information.

[0132] It should be understood that for the encoding of non-lidar point cloud planar position information, the predictive encoding of the planar position information may include:

[0133] (a) Using the occupancy information of neighboring nodes, the plane position information of the current node is predicted to be three elements: predicted as low plane, predicted as high plane, and unpredictable;

[0134] (b) Spatial distance between nodes at the same partition depth and coordinates as the current node and the current node: “near” and “far”;

[0135] (c) If the node at the same partition depth and the same coordinates as the current node is a plane, determine the plane position of the node;

[0136] (d) Coordinate dimension (i=0, 1, 2).

[0137] It should be noted that in an embodiment of the present application, after determining the spatial distance between the node at the same division depth and the same coordinates as the current node and the current node, if the spatial distance is less than the preset distance threshold, then the spatial distance can be determined to be "near"; or, if the spatial distance is greater than the preset distance threshold, then the spatial distance can be determined to be "far".

[0138] For example, Figure 10 shows a schematic diagram of neighboring nodes at the same partition depth and coordinates. As shown in Figure 10, the bold large cube represents the parent node, the small grid-filled cube inside it represents the current node, and the vertex position of the current node is shown. The small white-filled cube represents neighboring nodes at the same partition depth and coordinates. The distance between the current node and the neighboring node is the spatial distance, which can be judged as "near" or "far." In addition, if the neighboring node is a plane, the planar position of the neighboring node is also required.

[0139] In this way, as shown in Figure 10, the current node is a small cube filled with a grid, and the neighboring node is a small cube filled with white at the same octree partition depth level and the same vertical coordinate, and the distance between the two nodes is judged as "near" and "far", and the plane position of the reference node is used.

[0140] Furthermore, in an embodiment of the present application, FIG11 shows a schematic diagram of a current node being located at a low plane position of a parent node. As shown in FIG11 , (a), (b), and (c) show three examples of the current node being located at a low plane position of a parent node. Specific descriptions are as follows:

[0141] ① If any of the child nodes 4 to 7 of the point fill node is occupied, and all the grid fill nodes are not occupied, it is very likely that there is a plane in the current node (filled with a slash), and the plane is located lower.

[0142] ② If the child nodes 4 to 7 of the point fill node are not occupied, and any grid fill node is occupied, it is very likely that there is a plane in the current node (filled with diagonal lines), and the plane is located higher.

[0143] ③ If the child nodes 4 to 7 of the point fill node are all empty nodes and the grid fill nodes are all empty nodes, the plane position cannot be inferred and is therefore marked as unknown.

[0144] ④ If any of the child nodes 4 to 7 of the point fill node is occupied and any of the grid fill nodes is occupied, the plane position cannot be inferred at this time, so it is marked as unknown.

[0145] In an embodiment of the present application, FIG12 shows a schematic diagram of a current node being located at a high plane position of a parent node. As shown in FIG12, (a), (b), and (c) show three examples of the current node being located at a high plane position of a parent node. The specific description is as follows:

[0146] ① If any of the child nodes 4 to 7 of the grid fill node is occupied, and the point fill node is not occupied, it is very likely that there is a plane in the current node (filled with diagonal lines), and the plane position is low.

[0147] ② If the child nodes 4 to 7 of the grid fill node are not occupied, and the point fill node is occupied, it is very likely that there is a plane in the current node (filled with a slash), and the plane position is higher.

[0148] ③If the child nodes 4 to 7 of the grid fill node are all unoccupied, and the point fill node is unoccupied, the plane position cannot be inferred at this time, so it is marked as unknown.

[0149] ④ If one of the child nodes 4 to 7 of the grid fill node is occupied and the point fill node is occupied, the plane position cannot be inferred at this time and is therefore marked as unknown.

[0150] It should also be understood that, with respect to the coding of the laser radar point cloud plane position information, FIG13 shows a schematic diagram of the predictive coding of the laser radar point cloud plane position information. As shown in FIG13, when the laser radar emission angle is θ bottom When , it can be mapped to the bottom virtual plane; when the laser radar emission angle is θ top At this time, it can be mapped to the high plane (Top virtual plane).

[0151] That is, by using the laser radar acquisition parameters to predict the plane position of the current node, and by using the position where the current node intersects with the laser ray to quantize the position into multiple intervals, the final result is the context information of the plane position of the current node. The specific calculation process is as follows: Assume that the coordinates of the laser radar are (x Lidar ,y Lidar ,z Lidar ), the geometric coordinates of the current node are (x, y, z), then first calculate the vertical tangent value tanθ of the current node relative to the lidar, the calculation formula is as follows:

[0152] Furthermore, because each laser has a certain offset angle relative to the laser radar, it is also necessary to calculate the relative tangent value tanθ of the current node relative to the laser corr,L , the specific calculation is as follows:

[0153] Finally, the relative tangent value tanθ of the current node will be used corr,L To predict the plane position of the current node, as follows, assuming that the tangent value of the lower boundary of the current node is tan(θ bottom ), the tangent value of the upper boundary is tan(θ top ), according to tanθ corr,L The plane position is quantized into four quantization intervals, that is, the context information of the plane position is determined.

[0154] However, the octree-based geometric information coding mode only has an efficient compression rate for points that are correlated in space. For points that are isolated in the geometric space, the use of the Direct Coding Model (DCM) can greatly reduce the complexity. For all nodes in the octree, the use of DCM is not indicated by flag information, but is inferred from the parent node and neighbor information of the current node. There are three ways to determine whether the current node is eligible for DCM coding, as follows:

[0155] (1) The current node has no sibling child nodes, that is, the parent node of the current node has only one child node, and the parent node of the parent node of the current node has only two occupied child nodes, that is, the current node has at most one neighbor node.

[0156] (2) The parent node of the current node has only one child node, the current node, and the six neighbor nodes that share a face with the current node are all empty nodes.

[0157] (3) The number of sibling nodes of the current node is greater than 1.

[0158] Exemplarily, FIG14 provides a schematic diagram of IDCM coding. If the current node does not have the DCM coding qualification, it will be divided into octrees. If it has the DCM coding qualification, the number of points contained in the node will be further determined. When the number of points is less than a threshold (for example, 2), the node will be DCM-encoded, otherwise the octree division will continue. When the DCM coding mode is applied, it is first necessary to encode whether the current node is a true isolated point, that is, IDCM_flag. When IDCM_flag is true, the current node is DCM-encoded, otherwise octree coding is still used. When the current node meets the DCM coding requirements, the DCM coding mode of the current node needs to be encoded. There are currently two DCM modes: (a) there is only one point (or multiple points, but they are duplicate points); (b) there are two points. Finally, the geometric information of each point needs to be encoded. Assuming that the side length of the node is 2 d When encoding each component of the node's geometric coordinates, d bits are required, and these bits are directly encoded into the bitstream. It is important to note that when encoding LiDAR point clouds, the efficiency of geometric information coding can be further improved by predictively encoding the three-dimensional coordinate information using LiDAR acquisition parameters.

[0159] Furthermore, the IDCM encoding process is described in detail below.

[0160] When the current node meets the DCM encoding mode, the number of points of the current node, numPoints, is encoded first; the number of points of the current node is encoded according to different DirectModes:

[0161] ●If the current node does not meet the requirements of the DCM node, exit directly (that is, the number of points is greater than 2 points and it is not a duplicate point).

[0162] ●If the number of points numPonts in the current node is less than or equal to 2, the encoding process is as follows:

[0163] 1) First encode whether the numPonts of the current node is greater than 1;

[0164] 2) If the current node has only one point and the geometry coding environment is geometry lossless coding, it is necessary to encode the second point of the current node to ensure that it is not a duplicate point.

[0165] ●If the number of points numPonts in the current node is greater than 2, the encoding process is as follows:

[0166] 1) First encode the numPonts of the current node to be less than or equal to 1;

[0167] 2) Secondly, it is encoded that the second point of the current node is a repeated point, and then it is encoded whether the number of repeated points of the current node is greater than 1. When the number of repeated points is greater than 1, the remaining number of repeated points needs to be exponentially decoded.

[0168] After encoding the number of points in the current node, the coordinate information of the points contained in the current node is encoded. The following will introduce the lidar point cloud and the human eye point cloud in detail.

[0169] (1) Point cloud facing the human eye.

[0170] (1) If the current node contains only one point, the geometric information of the point in three dimensions will be directly encoded (Bypass coding);

[0171] (2) If the current node contains two points, the first coordinate axis dirextAxis will be obtained by using the geometric coordinates of the points. It should be noted that the coordinate axes currently compared only include the x-axis and y-axis, not the z-axis. Assuming that the geometric coordinates of the current node are nodePos, the judgment method is as follows: dirextAxis = ! (nodePos[0] <nodePos[1])

[0172] That is, the axis with the smallest node coordinate geometry position will be used as the priority encoding axis dirextAxis, and then the geometry information of the priority encoding axis dirextAxis will be encoded as follows. Assume that the encoding geometry bit depth corresponding to the priority encoding axis is nodeSizeLog2, and assume that the coordinates of the two points are pointPos[0] and pointPos[1]. The specific encoding process is as follows:

[0173] After encoding the priority axis dirextAxis, continue to directly encode the geometric coordinates of the current node. Assuming that the remaining encoding bit depth of each point is nodeSizeLog2, the specific encoding process is as follows: for(int axisIdx=0;axisIdx<3;++axisIdx) for(int mask=(1<<nodeSizeLog2[axisIdx])> >1;mask;mask>>1) encodePosBit(!!(pointPos[axisIdx]&mask)).

[0174] (2) LiDAR point cloud.

[0175] If the current node contains two points, the priority coded coordinate axis dirextAxis will be obtained first by using the geometric coordinates of the points. Assuming that the geometric coordinates of the current node are nodePos, the judgment method is as follows: dirextAxis = ! (nodePos[0] <nodePos[1])

[0176] That is, the axis with the smaller node coordinate geometry position will be used as the priority encoding axis dirextAxis. It should be noted that the currently compared coordinate axes only include the x-axis and y-axis, not the z-axis. Then, the geometric information of the priority encoded coordinate axis dirextAxis is first encoded as follows, assuming that the encoding geometry bit depth corresponding to the priority encoded axis is nodeSizeLog2, and assuming that the coordinates of the two points are pointPos[0] and pointPos[1]. The specific encoding process is as follows:

[0177] After encoding the priority axis dirextAxis, the geometric coordinates of the current node are encoded.

[0178] Since the laser radar point cloud can obtain the acquisition parameters of the laser radar point cloud, the geometric coordinate information of the current node can be predicted by using it, thereby further improving the efficiency of the geometric information encoding of the point cloud. Similarly, first use the geometric information nodePos of the current node to obtain a directly encoded main axis direction, and then use the geometric information of the encoded direction to predict the geometric information of another dimension. Also assuming that the axis direction of the direct encoding is directAxis, and assuming that the bit depth of the direct encoding is nodeSizeLog2, the encoding method is as follows: for(int mask=(1<<nodeSizeLog2)> >1;mask;mask>>1) encodePosBit(!!(pointPos[directAxis]&mask)).

[0179] It should be noted here that all geometric accuracy information in the directAxis direction will be encoded here.

[0180] For example, Figure 15 provides a schematic diagram of coordinate transformation for obtaining point clouds using a rotating laser radar. In the Cartesian coordinate system, the (x, y, z) coordinates of each node can be converted to Indicates. In addition, the laser scanner can perform laser scanning at a preset angle, and different θ(i) can be obtained under different values ​​of i. For example, when i is equal to 1, θ(1) can be obtained, and the corresponding scanning angle is -15°; when i is equal to 2, θ(2) can be obtained, and the corresponding scanning angle is -13°; when i is equal to 10, θ(10) can be obtained, and the corresponding scanning angle is +13°; when i is equal to 9, θ(19) can be obtained, and the corresponding scanning angle is +15°.

[0181] In this way, after encoding all the precision of the directAxis coordinate direction, the LaserIdx corresponding to the current point will be calculated first, that is, the pointLaserIdx number in Figure 15, and the LaserIdx of the current node, that is, nodeLaserIdx; secondly, the LaserIdx of the node, that is, nodeLaserIdx, will be used to predict the LaserIdx of the point, that is, pointLaserIdx. The calculation method of the LaserIdx of the node or point is as follows. Assuming that the geometric coordinates of the point are pointPos, the starting coordinates of the laser ray are LidarOrigin, and assuming that the number of Lasers is LaserNum, the tangent value of each Laser is tanθ i , the vertical offset position of each Laser is Z i ,but:

[0182] After calculating the current point's LaserIdx, the LaserIdx of the current node is first used to predictively encode the pointLaserIdx. After encoding the current point's LaserIdx, the three-dimensional geometric information of the current point is predictively encoded using the LiDAR acquisition parameters.

[0183] For example, FIG16 shows a schematic diagram of predictive coding in the X-axis or Y-axis direction. As shown in FIG16 , the box filled with a grid represents the current node, and the box filled with a slash represents the already coded node. Here, the LaserIdx corresponding to the current point is first used to obtain the corresponding predicted value of the horizontal azimuth, that is, Secondly, the node geometry information corresponding to the current point is used to obtain the horizontal azimuth angle corresponding to the node Assuming that the geometric coordinates of the node are nodePos, the calculation method between the horizontal azimuth angle φ and the node geometric information is as follows:

[0184] By using the acquisition parameters of the laser radar, we can get the number of rotation points of each laser, numPoints, which represents the number of points obtained by each laser ray rotating one circle. Then, we can use the number of rotation points of each laser to calculate the rotation angular velocity deltaPhi of each laser. The calculation method is as follows:

[0185] Furthermore, using the horizontal azimuth angle of the node And the horizontal azimuth of the previous Laser code point corresponding to the current point Calculate the predicted horizontal azimuth angle corresponding to the current point That is, the predicted value of the horizontal azimuth angle as shown in Figure 17A and Figure 17B. Figure 17A shows a schematic diagram of predicting the angle of the Y plane through the horizontal azimuth angle, and Figure 17B shows a schematic diagram of predicting the angle of the X plane through the horizontal azimuth angle. Here, the predicted value of the horizontal azimuth angle corresponding to the current point is The calculation is as follows:

[0186] For example, FIG18 shows another schematic diagram of predictive coding in the X-axis or Y-axis direction. As shown in FIG18 , the portion filled with a grid (left side) represents a low plane, and the portion filled with dots (right side) represents a high plane. Indicates the low plane horizontal azimuth of the current node, Indicates the horizontal azimuth of the high plane of the current node, Indicates the predicted horizontal azimuth angle corresponding to the current node.

[0187] Thus, by using the predicted value of the horizontal azimuth and the low plane horizontal azimuth of the current node and the high plane horizontal azimuth To predict the geometric information of the current node. The details are as follows: int context=(angLel≥0&&angLeR≥0)||(angLel<0&&angLeR<0)? 0:2; int minAngle=std∷min(abs(angLel),abs(angLeR)); int maxAngle=std∷max(abs(angLel),abs(angLeR)); context+=maxAngle>minAngle? 0:1; context+=maxAngle>minAngle? 0:4.

[0188] After encoding the LaserIdx of the point, the Z-axis direction of the current point will be predicted using the LaserIdx corresponding to the current point. That is, the depth information radius of the radar coordinate system is calculated by using the x and y information of the current point. Then, the tangent value of the current point and the vertical offset are obtained using the LaserIdx of the current point. The predicted value of the Z-axis direction of the current point, namely Z_pred, can be obtained. The details are as follows: int tanTheta=tanθ laserIdx ; int zOffset = Z laserIdx ; Z_pred=radius×tanTheta-zOffset.

[0189] Furthermore, Z_pred is used to perform predictive coding on the geometric information of the current point in the Z-axis direction to obtain the prediction residual Z_res, and finally Z_res is encoded.

[0190] It is important to note that when partitioning nodes into leaf nodes, the number of duplicate points in the leaf nodes must be encoded in the case of lossless geometric coding. Ultimately, the placeholder information for all nodes is encoded to generate a binary bitstream. Furthermore, G-PCC currently introduces a plane coding mode. During the geometric partitioning process, it determines whether the child nodes of the current node are in the same plane. If the child nodes of the current node meet the condition of being in the same plane, the child nodes of the current node are represented by that plane.

[0191] For octree-based geometric decoding, the decoder follows a breadth-first traversal. Before decoding each node's occupancy information, it first uses the reconstructed geometric information to determine whether the current node is for plane decoding or IDCM decoding. If the current node meets the requirements for plane decoding, it first decodes the plane identifier and plane position information of the current node. Then, based on the plane information, it decodes the current node's occupancy information. If the current node meets the requirements for IDCM decoding, it first decodes whether the current node is a true IDCM node. If so, it continues to parse the DCM decoding mode of the current node, then obtains the number of points in the current DCM node, and finally decodes the geometric information of each point. For nodes that do not meet either plane decoding or DCM decoding requirements, the current node's occupancy information is decoded. By continuously parsing in this way, the placeholder code of each node is obtained, and the node is continuously partitioned until a 1×1×1 unit cube is obtained. The number of points contained in each leaf node is parsed, and the geometrically reconstructed point cloud information is finally recovered.

[0192] The following is a detailed introduction to the IDCM decoding process.

[0193] Similar to the processing at the encoding end, we first use prior information to determine whether the node should start IDCM. The starting conditions of IDCM are as follows:

[0194] (1) The current node has no sibling child nodes, that is, the parent node of the current node has only one child node, and the parent node of the parent node of the current node has only two occupied child nodes, that is, the current node has at most one neighbor node.

[0195] (2) The parent node of the current node has only one child node, the current node, and the six neighbor nodes that share a face with the current node are all empty nodes.

[0196] (3) The number of sibling nodes of the current node is greater than 1.

[0197] Furthermore, when a node meets the conditions for DCM coding, it is first decoded to determine whether the current node is a true DCM node, that is, IDCM_flag; when IDCM_flag is true, the current node adopts DCM coding, otherwise it still adopts octree coding.

[0198] Next, decode the number of points numPoints of the current node. The specific decoding method is as follows:

[0199] i) First decode whether numPonts of the current node is greater than 1;

[0200] ii) If the numPonts of the current node is greater than 1, continue decoding to see if the second point is a duplicate point; if the second point is not a duplicate point, it can be implicitly inferred that the second type of DCM mode contains only two points;

[0201] iii) If the numPonts of the current node obtained by decoding is less than or equal to 1, continue decoding to see if the second point is a repeated point; if the second point is not a repeated point, it can be implicitly inferred that the second type of DCM pattern is satisfied, which contains only one point; if the second point obtained by decoding is a repeated point, it can be inferred that the third type of DCM pattern is satisfied, which contains multiple points, but they are all repeated points, then continue decoding to see if the number of repeated points is greater than 1 (entropy decoding), and if it is greater than 1, continue decoding the number of remaining repeated points (using exponential Columbus decoding).

[0202] If the current node does not meet the requirements of the DCM node, it will exit directly (that is, the number of points is greater than 2 points and it is not a duplicate point).

[0203] After decoding the number of points in the current node, the coordinate information of the points contained in the current node is decoded. The following will introduce the lidar point cloud and the human eye point cloud in detail.

[0204] (1) Point cloud facing the human eye.

[0205] (1) If the current node contains only one point, the geometric information of the point in three dimensions will be directly decoded (Bypass coding);

[0206] (2) If the current node contains two points, the first decoded coordinate axis dirextAxis will be obtained by using the geometric coordinates of the points. It should be noted that the coordinate axes currently compared only include the x and y axes, not the z axis. Assuming that the geometric coordinates of the current node are nodePos, the judgment method is as follows: dirextAxis = ! (nodePos[0] <nodePos[1])

[0207] That is, the axis with the smallest node coordinate geometry position will be used as the priority decoding axis dirextAxis, and then the geometry information of the priority decoding axis dirextAxis will be decoded first in the following way. Assume that the geometry bit depth to be decoded corresponding to the priority decoding axis is nodeSizeLog2, and assume that the coordinates of the two points are pointPos[0] and pointPos[1]. The specific encoding process is as follows:

[0208] After decoding the prioritized axis dirextAxis, the geometric coordinates of the current point are directly decoded. Assuming the remaining encoding bit depth of each point is nodeSizeLog2 and the coordinate information of the point is pointPos, the specific decoding process is as follows:

[0209] (2) LiDAR point cloud.

[0210] If the current node contains two points, the priority decoding coordinate axis dirextAxis will be obtained first by using the geometric coordinates of the points. Assuming that the geometric coordinates of the current node are nodePos, the judgment method is as follows: dirextAxis = ! (nodePos[0] <nodePos[1]) (11)

[0211] That is, the axis with the smaller node coordinate geometry position will be used as the priority decoding axis dirextAxis. It should be noted that the currently compared coordinate axes only include the x-axis and y-axis, not the z-axis. Secondly, the priority encoded coordinate axis dirextAxis geometry information is first decoded as follows, assuming that the encoding geometry bit depth corresponding to the priority decoded axis is nodeSizeLog2, and assuming that the coordinates of the two points are pointPos[0] and pointPos[1]. The specific encoding process is as follows:

[0212] After decoding the priority coordinate axis dirextAxis, the geometric coordinates of the current point are decoded.

[0213] Similarly, we first use the current node's geometry information nodePos to get a direct decoding main axis direction, and then use the geometry information of the decoded direction to decode the geometry information of the other dimension. Assuming that the axis direction of direct decoding is directAxis and the bit depth to be decoded in direct decoding is nodeSizeLog2, the decoding method is as follows:

[0214] It should be noted here that all geometric accuracy information in the directAxis direction will be decoded here.

[0215] After decoding all the accuracies of the directAxis coordinate direction, the LaserIdx of the current node, i.e., nodeLaserIdx, is calculated first. Secondly, the LaserIdx of the node, i.e., nodeLaserIdx, is used to predict and decode the LaserIdx of the point, i.e., pointLaserIdx. The calculation method of the LaserIdx of the node or point is the same as that of the encoding end. Finally, the predicted residual information of the LaserIdx of the current point and the LaserIdx of the node is decoded to obtain ResLaserIdx. The decoding method is as follows: PointLaserIdx = nodeLaserIdx + ResLaserIdx

[0216] After decoding the LaserIdx of the current point, the three-dimensional geometric information of the current point is predicted and decoded using the acquisition parameters of the laser radar. The specific algorithm is as follows:

[0217] As shown in Figure 11, the LaserIdx corresponding to the current point is first used to obtain the corresponding predicted value of the horizontal azimuth angle, that is, Secondly, the node geometry information corresponding to the current point is used to obtain the horizontal azimuth angle φ corresponding to the node nodeAssuming that the geometric coordinates of the node are nodePos, the calculation method between the horizontal azimuth angle φ and the node geometric information is as follows:

[0218] By using the acquisition parameters of the laser radar, we can get the number of rotation points of each laser, numPoints, which represents the number of points obtained by each laser ray rotating one circle. Then, we can use the number of rotation points of each laser to calculate the rotation angular velocity deltaPhi of each laser. The calculation method is as follows:

[0219] Furthermore, using the horizontal azimuth angle of the node And the horizontal azimuth of the previous Laser code point corresponding to the current point Calculate the predicted horizontal azimuth angle corresponding to the current point That is, the predicted value of the horizontal azimuth angle as shown in Figures 17A and 17B. The calculation method is as follows:

[0220] Thus, by using the predicted value of the horizontal azimuth and the low plane horizontal azimuth of the current node and the horizontal azimuth of the high plane To predict and decode the geometric information of the current node. The details are as follows: int context=(angLel≥0&&angLeR≥0)||(angLel<0&&angLeR<0)? 0:2; int absAngleL=abs(angLel); int absAngleR=abs(angLeR); context+=absAngleL>absAngleR? 0:1; context+=maxAngle>minAngle<<1? 4:0.

[0221] After decoding the LaserIdx of the completed point, the Z-axis direction of the current point will be predicted and decoded using the LaserIdx corresponding to the current point. That is, the depth information radius of the radar coordinate system is calculated by using the x and y information of the current point. Then, the tangent value of the current point and the vertical offset are obtained using the LaserIdx of the current point. The predicted value of the Z-axis direction of the current point, namely Z_pred, can be obtained. The details are as follows: int tanTheta=tanθ laserIdx ; int zOffset = Z laserIdx; Z_pred=radius×tanTheta-zOffset.

[0222] Furthermore, the decoded Z_res and Z_pred are used to reconstruct and restore the geometric information of the current point in the Z-axis direction.

[0223] For triangle soup (trisoup)-based geometric information coding, geometric partitioning must also be performed first in the trisoup-based geometric information coding framework. However, unlike geometric information coding based on binary trees, quadtrees, and octrees, this method does not require step-by-step partitioning of the point cloud into unit cubes with side lengths of 1×1×1. Instead, the partitioning stops when the sub-blocks (blocks) have a side length of W. Based on the surface formed by the distribution of the point cloud in each block, the surface and the twelve edges of the block are obtained. The vertex coordinates of each block are encoded in sequence to generate a binary code stream.

[0224] For trisoup-based point cloud geometry reconstruction, when performing point cloud geometry reconstruction at the decoding end, the vertex coordinates are first decoded to complete the triangle face reconstruction. This process is shown in Figures 19A, 19B, and 19C. Among them, there are three intersection points (v1, v2, v3) in the block shown in Figure 19A. The set of triangle faces formed by these three intersection points in a certain order is called triangle soup, or trisoup, as shown in Figure 19B. Afterwards, sampling is performed on the triangle face set, and the obtained sampling points are used as the reconstructed point cloud within the block, as shown in Figure 19C.

[0225] For predictive geometry coding (PredGeomTree), the following steps are involved: first, sort the input point cloud. Currently, the sorting methods used include unordered, Morton order, azimuth order, and radial distance order. At the encoding end, the prediction tree structure is established using two different methods: KD-Tree (high-latency slow mode) and low-latency fast mode (using lidar calibration information). When using lidar calibration information, each point is divided into different lasers, and a prediction tree structure is established according to the different lasers. Next, based on the structure of the prediction tree, each node in the prediction tree is traversed, and the geometric position information of the node is predicted by selecting different prediction modes to obtain the prediction residual, and the geometric prediction residual is quantized using the quantization parameter. Finally, through continuous iteration, the prediction residual of the prediction tree node position information, the prediction tree structure, and the quantization parameter are encoded to generate a binary code stream.

[0226] For geometric decoding based on the prediction tree, the decoding end reconstructs the prediction tree structure by continuously parsing the bit stream. Secondly, the geometric position prediction residual information and quantization parameters of each prediction node are obtained through parsing, and the prediction residual is dequantized to restore the reconstructed geometric position information of each node, finally completing the geometric reconstruction at the decoding end.

[0227] After the geometric encoding is completed, the geometric information needs to be reconstructed. At present, attribute encoding is mainly performed on color information. First, the color information is converted from the RGB color space to the YUV color space. Then, the point cloud is recolored using the reconstructed geometric information so that the unencoded attribute information corresponds to the reconstructed geometric information. In color information encoding, there are two main transformation methods. One is the distance-based lifting transformation that relies on LOD partitioning, and the other is to directly perform RAHT transformation. Both methods will convert the color information from the spatial domain to the frequency domain, and obtain high-frequency coefficients and low-frequency coefficients through transformation. Finally, the coefficients are quantized and encoded to generate a binary code stream, as shown in Figures 4A and 4B.

[0228] Furthermore, when using geometric information to predict attribute information, Morton codes can be used to perform nearest neighbor search. The Morton code corresponding to each point in the point cloud can be obtained from the geometric coordinates of the point. The specific method for calculating the Morton code is described below. For a three-dimensional coordinate represented by a d-bit binary number for each component, its three components can be expressed as:

[0229] in, The highest bits of x, y, and z respectively To the lowest position The corresponding binary value. The Morton code M is x, y, z starting from the highest bit, arranged in sequence To the lowest bit, the calculation formula of M is as follows:

[0230] in, The highest bit of M To the lowest position After obtaining the Morton code M of each point in the point cloud, the points in the point cloud are arranged in ascending order of the Morton code, and the weight value w of each point is set to 1.

[0231] It can also be understood that for the G-PCC codec framework, the general test conditions are as follows:

[0232] (1) There are 4 test conditions:

[0233] Condition 1: The geometric position is limited and the attributes are lost;

[0234] Condition 2: Geometric position lossless, attribute lossy;

[0235] Condition 3: Geometric position lossless, attribute loss limited;

[0236] Condition 4: Geometric position and attributes are lossless.

[0237] (2) The general test sequence includes four categories: Cat1A, Cat1B, Cat3-fused, and Cat3-frame. Among them, Cat2-frame point cloud only contains reflectance attribute information, Cat1A and Cat1B point clouds only contain color attribute information, and Cat3-fused point cloud contains both color and reflectance attribute information.

[0238] (3) Technical routes: There are two types in total, which are distinguished by the algorithm used for geometric compression.

[0239] Technical route 1: Octree encoding branch.

[0240] At the encoding end, the bounding box is divided into sub-cubes in sequence, and the non-empty sub-cubes (containing points in the point cloud) are divided again until the leaf node obtained is a 1×1×1 unit cube. In the case of geometric lossless coding, the number of points contained in the leaf node needs to be encoded, and finally the geometric octree encoding is completed to generate a binary code stream.

[0241] At the decoding end, the decoding end obtains the placeholder code of each node by continuously parsing in the order of breadth-first traversal, and continuously divides the nodes in turn until a 1×1×1 unit cube is obtained. In the case of geometric lossless decoding, it is necessary to parse the number of points contained in each leaf node and finally recover the geometrically reconstructed point cloud information.

[0242] Technical route 2: prediction tree encoding branch.

[0243] On the encoding side, the prediction tree structure is established using two different methods: based on KD-Tree (high-latency slow mode) and using lidar calibration information (low-latency fast mode). Using lidar calibration information, each point can be divided into different lasers, and the prediction tree structure is established according to different lasers. Next, based on the structure of the prediction tree, each node in the prediction tree is traversed, and the geometric position information of the node is predicted by selecting different prediction modes to obtain the prediction residual, and the geometric prediction residual is quantized using the quantization parameter. Finally, through continuous iteration, the prediction residual of the prediction tree node position information, the prediction tree structure, and the quantization parameters are encoded to generate a binary code stream.

[0244] At the decoding end, the decoding end continuously analyzes the bitstream to reconstruct the prediction tree structure. Secondly, it obtains the geometric position prediction residual information and quantization parameters of each prediction node through analysis, and inverse quantizes the prediction residual to restore the reconstructed geometric position information of each node, finally completing the geometric reconstruction at the decoding end.

[0245] It should also be noted that as shown in FIG. 4A or FIG. 4B, the current G-PCC coding framework includes three attribute coding methods: Predicting Transform (PT), Lifting Transform (LT), and Region Adaptive Hierarchical Transform (RAHT). Among them, the first two perform point cloud prediction coding based on the generation order of LODs, while RAHT adaptively transforms the attribute information from bottom to top according to the construction levels of the octree. The following will specifically introduce these three point cloud attribute coding methods.

[0246] (a) Predictive coding of point cloud attribute information.

[0247] Currently, the attribute prediction module of G-PCC adopts a nearest neighbor attribute prediction coding scheme based on a Level-of-details (LoDs) structure. The construction methods of LODs include a distance-based LOD construction scheme, a fixed sampling rate-based LOD construction scheme, and an octree-based LOD construction scheme, etc. In the distance threshold-based LOD construction scheme, before constructing the LOD, the point cloud is first sorted by Morton to ensure strong attribute correlation between adjacent points. FIG. 20 is a schematic diagram of a distance-based LOD construction process. As shown in FIG. 20, according to L Manhattan distances (dl) preset by the user in advance, l = 0, 1, … L-1; the point cloud is divided into L different point cloud detail levels (Rl), l = 0, 1, … L-1, where (dl)l = 0, 1, … L-1 satisfies dl < dl-1. The LOD construction process is as follows:

[0248] (1) First, mark all points in the point cloud as unvisited, and establish a set V to store the set of visited points; (2) For each iteration l, traverse the points in the point cloud. If the current point has been visited, ignore it. Otherwise, calculate the minimum distance D from the current point to the set V. If D < dl, ignore the point; otherwise, mark the current point as visited and add the current point to the refinement level Rl and the set V; (3) The points in the level of detail LODl are composed of the points in the refinement levels R0, R1, R2…Rl; (4) Continuously repeat the above steps until all points are marked as visited.

[0249] Based on the LOD structure, the attribute value of each point is linearly weighted predicted using the attribute reconstruction values ​​of points in the same or higher LOD layer. The maximum number of reference prediction neighbors is determined by the encoder's high-level syntax elements. For each point attribute, the encoder uses a rate-distortion optimization algorithm to select a weighted prediction using the attributes of the N nearest neighbors found, or a single nearest neighbor for prediction. Finally, the selected prediction mode and prediction residual are encoded.

[0250] Among them, N represents the number of predicted points in the nearest neighbor point set of point i, Pi represents the sum of the N nearest neighbor points of point i, Dm represents the spatial geometric distance from the nearest neighbor point m to the current point i, Attrm represents the attribute value of the nearest neighbor point m after reconstruction, Attr i ′ represents the attribute prediction value of the current point i, and the number of points N is a preset value.

[0251] To balance attribute coding efficiency and parallel processing between different LOD layers, a switch is introduced in the encoder's high-level syntax elements to control whether to use intra-LOD prediction. If turned on, intra-LOD prediction is enabled, allowing predictions to be made using points within the same LOD layer. Note that when the number of LOD layers is 1, intra-LOD prediction is always used.

[0252] Figure 21 shows a visualization of the LOD generation process. This provides a subjective example of the distance-based LOD generation process. Specifically (from left to right): points in the first layer represent the outer contours of the point cloud; as the number of detail layers increases, the point cloud details become increasingly clear.

[0253] Figure 22 is a schematic diagram of the attribute prediction encoding process. As shown in Figure 22, for the specific process of G-PCC attribute prediction, for the original point cloud, the three nearest neighbors of the Kth point are first searched, and then attribute prediction is performed. The difference between the attribute prediction value of the Kth point and the original attribute value of the Kth point is calculated to obtain the prediction residual of the Kth point. Quantization and arithmetic coding are then performed to finally generate the attribute bit rate.

[0254] (i) Optimal prediction value selection:

[0255] After the LOD is constructed, according to the generation order of LOD, the three nearest neighboring points of the current point to be encoded are first found from the encoded data points. The attribute reconstruction values ​​of these three nearest neighboring points are used as candidate prediction values ​​of the current point to be encoded; then, the optimal prediction value is selected from them according to the rate-distortion optimization (RDO). For example, when encoding the attribute value of point P2 in Figure 20, the prediction variable index of the attribute value of the nearest neighbor point P4 is set to 1; the attribute prediction variable indexes of the second nearest neighbor point P5 and the third nearest neighbor point P0 are set to 2 and 3 respectively; the prediction variable index of the weighted average of points P0, P5 and P4 is set to 0, as shown in Table 1; finally, RDO is used to select the best prediction variable. The formula for weighted average is as follows:

[0256] in, Represents the spatial geometric weight of the neighboring point j to the current point i:

[0257] Represents the attribute prediction value of the current point i, j represents the index of the three neighboring points, Represents the attribute value after reconstruction of the neighboring points, x i ,y i ,z i is the geometric position coordinate of the current point i, x ij ,y ij ,z ij is the geometric coordinate of the neighboring point j.

[0258] For example, Table 1 provides an example of candidate prediction item samples for an attribute code.

[0259] Table 1

[0260] (ii) Attribute prediction residuals and quantification:

[0261] The attribute prediction value of the current point i is obtained through the above prediction (k is the total number of points in the point cloud). Let (a i ) i∈0…k-1 is the original value of the attribute of the current point, then the attribute residual (r i ) i∈0…k-1 Denoted as:

[0262] Further quantify the prediction residuals:

[0263] Among them, Q iIt represents the quantized attribute residual of the current point i, Qs is the quantization step (Qs), which can be calculated by the quantization parameter QP (QP) specified by CTC.

[0264] (iii) The encoding end reconstructs the attribute value:

[0265] The purpose of reconstruction at the encoding end is to predict the subsequent points. Before reconstructing the attribute value, the residual must be dequantized. is the residual after inverse quantization:

[0266] and predicted value Add up to get the reconstruction value of point i

[0267] There are currently two main types of algorithms for attribute nearest neighbor search based on LOD partitioning: intra-frame nearest neighbor search and inter-frame nearest neighbor search. The inter-frame nearest neighbor search algorithm is detailed below, while the intra-frame nearest neighbor search can be divided into inter-layer nearest neighbor search and intra-layer nearest neighbor search.

[0268] (i) Intra-frame nearest neighbor search:

[0269] Intra-frame nearest neighbor search is divided into two algorithms: inter-layer nearest neighbor search and intra-layer nearest neighbor search. After LOD division, it resembles a pyramid structure, as shown in Figure 23.

[0270] In a specific implementation, for inter-layer nearest neighbor search, the pyramid structure is shown in FIG24. FIG25 is a pyramid structure for inter-layer nearest neighbor search.

[0271] Schematic diagram of the LOD construction process of neighbor search. As shown in Figure 25, different LOD layers are obtained based on geometric information division.

[0272] LOD0, LOD1 and LOD2 use the points in LOD0 to predict the attributes of the points in the next layer of LOD in the nearest neighbor search between layers

[0273] In the process.

[0274] The entire process of searching for the nearest neighbor within a frame is described in detail below.

[0275] During the entire LOD partitioning process, there are three sets: O(k), L(k), and I(k). Among them, k is the index of the LOD layer during LOD partitioning, and I(k) is the input point set during the current LOD layer partitioning. After LOD partitioning, the O(k) set and L(k) set are obtained. The O(k) set stores the sampling point set, and L(k) is the point set in the current LOD layer. The entire LOD partitioning process is as follows:

[0276] (1) Initialization.

[0277] if k=0,L(k)←{}; otherwise,L(k)←L(k-1);

[0278] O(k)←{};

[0279] (2) Using the LOD partitioning algorithm, the sampling points are stored in O(k), and the remaining points are divided into L(k);

[0280] (3) When the next iteration is performed, I←O(k).

[0281] It should be noted here that since the entire LOD division process is based on the Morton code, O(k), L(k) and I(k) store the Morton code index corresponding to the point.

[0282] When performing inter-layer nearest neighbor search, that is, the points in the L(k) set perform nearest neighbor search in the O(k) set, the search algorithm is as follows:

[0283] Taking the nearest neighbor search based on spatial relationships as an example, when predicting the current point P, the neighbor search is performed by using the parent block (Block B) corresponding to point P. As shown in Figure 26, points in the neighbor blocks that are coplanar or colinear with the current parent block are searched for attributes.

[0284] Figure 27A shows a schematic diagram of a coplanar spatial relationship, where there are 6 spatial blocks that have a relationship with the current parent block. Figure 27B shows a schematic diagram of a coplanar and colinear spatial relationship, where there are 18 spatial blocks that have a relationship with the current parent block. Figure 27C shows a schematic diagram of a coplanar, colinear, and co-point spatial relationship, where there are 26 spatial blocks that have a relationship with the current parent block.

[0285] First, the coordinates of the current point are used to obtain the corresponding spatial block. Second, a nearest neighbor search is performed in the previously encoded LOD layer to find the spatial blocks that are coplanar, colinear, and co-point with the current block to obtain the N nearest neighbors of the current point.

[0286] After performing coplanar, colinear, and co-point nearest neighbor searches, if the N nearest neighbors of the current point are still not found, the N nearest neighbors of the current point will be found based on a fast search algorithm. The specific algorithm is as follows:

[0287] As shown in Figure 28, when performing inter-attribute layer prediction, the geometric coordinates of the current point to be encoded are first used to obtain the Morton code corresponding to the current point. Secondly, based on the Morton code of the current point, the first reference point (j) with a value greater than the Morton code of the current point is found in the reference frame. Then, the nearest neighbor search is performed within the range of [j-searchRange, j+searchRange].

[0288] The rest of the specific algorithms for updating the nearest neighbor are the same as the inter-frame nearest neighbor search algorithm and will not be described in detail here. The specific algorithms will be mentioned in the inter-frame nearest neighbor search algorithm.

[0289] In another specific implementation, for the nearest neighbor search within a layer, Figure 29 shows a schematic diagram of the LOD structure of the nearest neighbor search within an attribute layer. As shown in Figure 29, if the intra-layer prediction algorithm is turned on, that is, the syntax element EnableRefferingSameLoD=1, then the nearest neighbor search within the layer can be allowed. For example, for the LOD1 layer, the nearest neighbor point of the current point P6 can be P1, which is not allowed in other layers; if the syntax element EnableRefferingSameLoD=0, then inter-layer search is allowed in other layers. For example, for the LOD1 layer, the nearest neighbor point of the current point P6 can be P4. That is to say, when the intra-layer prediction algorithm is turned on, the nearest neighbor search will be performed in the same layer LOD and the set of encoded points in the same layer to obtain the N nearest neighbors of the current point (the inter-layer nearest neighbor search is also performed).

[0290] When performing prediction within the attribute layer, a nearest neighbor search is performed based on a fast search algorithm. The specific algorithm is shown in Figure 30. The current point is represented by a grid. Assuming the Morton code index of the current point is i, the nearest neighbor search is performed in [i+1, i+searchRange]. The specific nearest neighbor search algorithm is consistent with the inter-frame block-based fast search algorithm and is not described in detail here.

[0291] (ii) Nearest neighbor search between frames:

[0292] Figure 28 is a schematic diagram of attribute inter-frame prediction. As shown in Figure 28, when performing attribute inter-frame prediction, the geometric coordinates of the current point to be encoded are first used to obtain the Morton code corresponding to the current point. Then, based on the Morton code of the current point, the first reference point (j) with a Morton code greater than the current point is found in the reference frame. Then, the nearest neighbor search is performed within the range [j-searchRange, j+searchRange].

[0293] The current nearest neighbor search within and between frames is based on block-based neighborhood search, as shown in Figure 31. As shown in Figure 31, when performing neighborhood search for the current point (Morton code index is i), the points in the reference frame are first divided into N (N=3) layers according to the Morton code. The specific division algorithm is as follows:

[0294] First layer: Assume that the points of the reference frame are numPoints, first divide the points in the reference frame into M (M=2 5 =32) points are divided into one block;

[0295] Second layer: Based on the first layer, the blocks of the first layer are also processed in the order of Morton code every M (M=2 5 =32) blocks are divided into one block;

[0296] The third layer: Based on the second layer, the blocks of the first layer are also processed in the order of the Morton code every M (M=2 5 =32) blocks are divided into one block;

[0297] Finally, the predicted structure shown in Figure 31 is obtained.

[0298] When performing attribute prediction based on the prediction structure shown in Figure 31, assuming that the Morton code index of the current point to be encoded is i, first obtain the first point in the reference frame whose Morton code is greater than or equal to the current point, with index j. Then, the block index of the reference point is calculated based on j. The specific calculation method is as follows:

[0299] First layer: BucketSize_0 = 2 5 =32;

[0300] Second layer: BucketSize_1 = 2 5 =32×BucketSize_0=1024;

[0301] Third layer: BucketSize_2 = 2 5 =32×BucketSize_1=32768.

[0302] Assume that the reference range in the prediction frame of the current point is [j-searchRange, j+searchRange], use j-searchRange to calculate the starting index of the third layer, and use j+searchRange to calculate the ending index of the third layer; secondly, first determine whether some blocks in the second layer need to be searched for the nearest neighbor in the blocks of the third layer, and then go to the second layer, and determine whether a search is needed for each block in the first layer. If some blocks in the first layer need to be searched for the nearest neighbor, then the midpoints of some blocks in the first layer will be judged point by point to update the nearest neighbor.

[0303] The following is an introduction to the algorithm based on index calculation block. Assuming that the Morton code index corresponding to the current point is index, then the index of the corresponding third-level block is: idx_2 = index / BucketSize_2

[0304] After obtaining the block index idx_2 of the third layer, the start index and end index of the block corresponding to the current block in the second layer can be obtained using idx_2: startIdx1=idx_2×BucketSize_1 endIdx=idx_2×BucketSize_1+BucketSize_1-1

[0305] Similarly, the index of the first layer block is obtained based on the index of the second layer block based on the same algorithm.

[0306] When performing a block-based nearest neighbor search, we first determine whether the current block needs to be searched for the nearest neighbor. This is called filtering the nearest neighbor search for the block. Each spatial block can be obtained through two variables: minPos and maxPos. MinPos represents the minimum value of the block, and maxPos represents the maximum value of the block.

[0307] Assume that the distance to the farthest point among the N nearest neighbors of the current point is Dist, the coordinates of the point to be encoded are (x, y, z), and the current block is represented by (minPos, maxPos), where minPos is the minimum value of the three dimensions of the bounding box and maxPos is the maximum value of the three dimensions of the bounding box. The distance D between the current point and the bounding box is calculated as follows: int dx = int(std::max(std::max(minPos[0]-point[0],0),point[0]-maxPos[0])); int dy = int(std::max(std::max(minPos[1]-point[1],0),point[1]-maxPos[1])); int dz = int(std::max(std::max(minPos[2]-point[2],0),point[2]-maxPos[2])); D = dx+dy+dz;

[0308] When D is less than or equal to Dist, the points in the current block will be traversed.

[0309] (b) Lifting transform encoding of point cloud attribute information.

[0310] Figure 32 is a schematic diagram of the encoding process of a lifting transform. The lifting transform also predicts the attributes of the point cloud based on LOD. The difference from the predictive transform is that the lifting transform first divides the LOD into high and low layers, predicts in the reverse order of the LOD generation layer, and introduces an update operator in the prediction process to update the quantized weights of the low-level LOD midpoints to improve the accuracy of the prediction. This is because the attribute values ​​of the low-level LOD midpoints are frequently used to predict the attribute values ​​of the high-level LOD midpoints, and the points in the low-level LOD should have greater influence.

[0311] Step 1: Segmentation process.

[0312] The segmentation process is to divide the complete LOD layer into a low LOD layer L(N) and a high LOD layer H(N). If a point cloud has three LOD layers, namely (LOD l ) l＝0,1,2 , after segmentation, LOD2 is the high LOD layer, denoted as H(N), (LOD l ) l＝0,1 It is the low LOD layer, denoted as L(N).

[0313] Step 2: Prediction process.

[0314] The point in the high-level LOD selects the attribute information of the nearest neighbor point from the low-level as the attribute prediction value P(N) of the current point to be coded. The prediction residual D(N) is recorded as: D(N) = H(N) - P(N)#

[0315] Step 3: Update process.

[0316] Update the attribute prediction residual D(N) in the high-level LOD to obtain U(N), and use U(N) to improve the attribute value of the midpoint of the low-level LOD, as shown in the following formula: L′(N)=L(N)+U(N)

[0317] The above process will iterate continuously until the lowest LOD according to the order of LOD from high to low.

[0318] Because the LOD-based prediction scheme makes points in the lower LOD layers more influential, the transformation scheme based on the lifting wavelet transform introduces quantization weights and updates the prediction residual based on the prediction residual D(N) and the distance between the prediction point and the adjacent points. Finally, the quantization weights used in the transformation process are used to adaptively quantize the prediction residual. It is important to note that the quantization weight value of each point can be determined by geometric reconstruction at the decoding end, so the quantization weights should not be encoded.

[0319] (c) Region-adaptive hierarchical transformation.

[0320] The Regional Adaptive Hierarchical Transform (RAHT) is a Haar wavelet transform that transforms point cloud attribute information from the spatial domain to the frequency domain, further reducing the correlation between point cloud attributes. Its main concept is to transform the nodes in each layer in the X, Y, and Z dimensions in a bottom-up manner according to the octree structure (as shown in Figure 34), and iterate until the root node of the octree. As shown in Figure 33, the basic concept is to perform a wavelet transform based on the hierarchical structure of the octree, associate attribute information with the octree nodes, and recursively transform the attributes of occupied nodes under the same parent node in a bottom-up manner, transforming the nodes in each layer in the X, Y, and Z dimensions until the root node of the octree is reached. During the hierarchical transformation process, the low-pass / low-frequency (DC) coefficients obtained after the transformation of the nodes in the same layer are passed to the nodes in the next layer for further transformation, while all high-pass / high-frequency (AC) coefficients can be encoded using an arithmetic coder.

[0321] During the transformation process, the DC coefficients (direct current components) of the transformed nodes at the same layer are passed to the previous layer for further transformation, while the AC coefficients (alternating current components) of each layer are quantized and encoded. The main transformation processes are described below.

[0322] FIG35A is a schematic diagram of a RAHT forward transformation process, and FIG35B is a schematic diagram of a RAHT inverse transformation process. For the transformation and inverse transformation process corresponding to RAHT, assuming that g′ L,2x,y,z and g′L,2x+1,y,z are the DC coefficients of two neighboring points in the L layer. After linear transformation, the information of the L-1 layer is the AC coefficient f′ L-1,x,y,z and DC coefficient g′ L-1,x,y,z ; Then, f′ L-1,x,y,z No more transformation will be performed, and quantization coding will be performed directly, g′ L-1,x,y,z The nearest neighbor will continue to be searched for transformation. If no neighbor is found, it will be directly passed to the L-2 layer. That is, the RAHT transformation is only effective for nodes with neighbor points. Nodes without neighbor points will be directly passed to the previous layer. In the above transformation process, g′ L,2x,y,z The weights (the number of non-empty child nodes in the node) corresponding to g′L, 2x+2, y, and z are w′ respectively. L,2x,y,z and w′L,2x+1,y,z (abbreviated as w′0 and w′1), g′ L-1,x,y,z The weight is w′ L-1,x,y,z , then the general transformation formula is:

[0323] Among them, T w0,w1 is the transformation matrix:

[0324] The transformation matrix will be updated as the weights corresponding to each point change adaptively. The above process will be iterated and updated continuously according to the partitioning structure of the octree until the root node of the octree is reached.

[0325] In a specific implementation method, for regional adaptive hierarchical intra-frame prediction transform coding, prediction can be performed based on RAHT transform coding. As shown in Figure 33, RAHT attribute transform is based on the order of the octree hierarchy, and the transformation is continuously performed from the voxel level until the root node is obtained, thereby completing the hierarchical transform coding of the entire attribute. In the prediction transform coding, attribute prediction transform coding is also performed based on the hierarchical order of the octree, but the transformation is continuously performed from the root node to the voxel level. In each RAHT attribute transformation process, attribute prediction transform coding is performed based on a 2×2×2 block. The specific example is shown in Figure 36. As shown in Figure 36, it can be seen that the grid filling block is the current block to be encoded, and the diagonal filling block is some neighboring blocks that are coplanar and colinear with the current block to be encoded. Among them, the attributes of the current block are normalized in the following way: A node =∑ p∈node attribute(p); w node =∑ p∈node 1={p∈node}; a node =A node / w node .

[0326] First, the attributes of the current block can be obtained by the attributes of the points contained in the current block, namely: A node By simply adding the attributes of the points in the current block, and then normalizing the attributes of the current block with the number of points in the current block, the mean value a of the current block attribute is obtained. node The attribute transform coding is performed using the mean value of the current block attribute. The specific coding process is shown in Figure 37.

[0327] As shown in Figure 37, the overall process of RAHT attribute prediction transform coding is shown. (a) is the current block and some coplanar and colinear neighboring blocks, (b) is the normalized block, (c) is the upsampled block, (d) is the attribute of the current block, and (e) is the attribute of the predicted block obtained by linear weighted fitting using the neighboring attributes of the current block. Finally, attribute transformation is performed on both to obtain DC and AC coefficients, and the AC coefficients are predictively coded.

[0328] The predicted attributes of the current block can be obtained by linear fitting as shown in Figure 38. As shown in Figure 38, 19 neighboring blocks of the current block are first obtained. Then, the spatial geometric distance between the neighboring blocks and each sub-block of the current block is used to perform a linear weighted prediction of the attributes of each sub-block. Finally, the predicted block attributes obtained using the linear weighting are transformed. The specific attribute transformation is shown in Figure 39.

[0329] In Figure 39, (d) represents the original value of the attribute, and the corresponding attribute transformation coefficient is as follows:

[0330] (e) represents the attribute prediction value, and the corresponding attribute transformation coefficient is as follows:

[0331] By subtracting the original value of the attribute from the predicted value of the attribute, the prediction residual can be obtained as follows:

[0332] In another specific implementation, region-adaptive hierarchical inter-frame prediction transform coding, in G-PCC attribute inter-frame prediction, a similar process is used for intra-frame prediction coding. First, a RAHT attribute transform coding structure is constructed based on geometric information. Specifically, transformations are performed continuously from the voxel level until the root node is reached, completing the hierarchical transform coding of the entire attribute. The intra-frame coding structure and inter-frame attribute coding structure are constructed in this way. See Figure 40 for details.

[0333] As shown in FIG40 , firstly, the geometric information of the current node to be coded is used to obtain the same-position prediction node of the node to be coded in the reference frame, and then the geometric information and attribute information of the reference node are used to obtain the predicted attribute of the current node to be coded.

[0334] The attribute prediction value of the current node to be encoded is obtained in the following two different ways:

[0335] ① The inter-frame prediction node of the current node is valid: that is, if the same-position node exists, the attribute of the predicted node is directly used as the attribute prediction value of the current node to be encoded;

[0336] ② The inter-frame prediction node of the current node is invalid: that is, the co-located node does not exist, then the attribute prediction value of the adjacent node in the frame is used as the attribute prediction value of the node to be encoded.

[0337] Finally, the attribute prediction value is used to predict the attribute of the current node to be encoded, thereby completing the predictive coding of the entire attribute.

[0338] In another specific implementation method 2, for regional adaptive hierarchical inter-frame prediction transform coding, in the G-PCC attribute inter-frame prediction coding scheme, unlike the above-mentioned inter-frame prediction coding scheme, if the inter-frame prediction coding scheme is started, the RAHT attribute transform coding structure is first constructed based on the geometric information of the current node to be coded, that is, the nodes are continuously merged at the voxel level until the root node of the entire RAHT transform tree is obtained, thereby completing the transform coding hierarchical structure of the entire attribute. Secondly, according to the RAHT attribute transform coding structure, the root node is divided to obtain N child nodes (N is less than or equal to 8) of each node. First, the attributes of the N child nodes are independently orthogonally transformed using the RAHT transform to obtain DC coefficients and AC coefficients, and then the AC coefficients of the N child nodes are subjected to attribute inter-frame prediction in the following manner:

[0339] The inter-frame prediction node of the current node is valid: that is, if the co-located node exists, the attribute of the predicted node is directly used as the attribute prediction value of the current node to be encoded; wherein, the current node to be encoded can also be understood as the current node.

[0340] The current node can find a node with exactly the same position as the current node in the reference frame cache: that is, if the co-located node exists, the AC coefficients of the M child nodes contained in the co-located node are directly used as the AC coefficient attribute prediction values ​​of the N child nodes of the current node.

[0341] If the AC coefficient of the predicted node is not zero: the AC coefficient of the predicted node is directly used as the AC coefficient prediction value;

[0342] If the AC coefficient of the prediction node is zero, the AC coefficient of the corresponding child node of the intra-frame prediction is used as the AC coefficient prediction value.

[0343] The inter-frame prediction node of the current node is invalid: that is, if the co-located node does not exist, the attribute prediction value of the adjacent node in the frame is used as the attribute prediction value of the node to be encoded.

[0344] In the aforementioned G-PCC-attribute RAHT inter-frame prediction coding, whether to enable RAHT inter-frame prediction coding is determined in the higher-level aps syntax element. When RAHT inter-frame prediction coding is enabled, either Implementation 1 or Implementation 2 is used. However, in the related RAHT inter-frame prediction coding, only unidirectional inter-frame prediction coding is used, meaning that the current node to be coded has only one reference node. This coding scheme presents the following major issues: 1. It only considers the temporal redundancy between the forward reference frame and the current frame to be coded, but not the temporal redundancy between the backward reference frame and the current frame to be coded. 2. In the related RAHT inter-frame coding scheme, whether Implementation 1 or Implementation 2, when determining whether to enable inter-frame prediction coding for the current node, even if inter-frame prediction coding is enabled for the current coding unit, it is necessary to consider whether each node has a co-located node in the reference frame. If a co-located node exists, inter-frame prediction coding is enabled for the current node; otherwise, only intra-frame prediction coding is used. Based on such a coding scheme, only inter-frame prediction coding with a unidirectional or single-frame prediction list is used. First, the temporal redundancy characteristics between adjacent frames are not fully utilized, and the temporal correlation between adjacent frames cannot be removed. Second, since point clouds are sparsely distributed data, in related coding schemes, some nodes cannot start the inter-frame prediction coding scheme because they cannot find the same-position node in the unidirectional reference frame, further reducing the coding efficiency between attribute frames.

[0345] Based on the above analysis, in an embodiment of the present application, a bidirectional reference inter-frame prediction coding scheme based on RAHT attribute transformation is provided. The scheme first introduces the reference of the bidirectional prediction list in the RAHT attribute inter-frame prediction coding, and secondly performs attribute inter-frame prediction coding on the attribute AC coefficient of the current node to be coded based on the reconstructed attribute / reconstructed AC coefficient of the bidirectional reference list. Compared with the above-mentioned prediction coding scheme, such a coding scheme can use the redundancy characteristics of the time slots between the current frame to be coded and the forward reference frame and the backward reference frame. In addition, for the node to be coded, since the number of nodes available for reference is changed from the original unidirectional prediction node to the bidirectional prediction node, for each node to be coded, the reference objects that can be selected for inter-frame prediction are increased compared with the original coding scheme, thereby more effectively removing the redundancy characteristics of the time slots between adjacent coded frames.

[0346] An embodiment of the present application provides an encoding method, which is applied to an encoder. FIG41 is a schematic diagram of an implementation flow of the encoding method provided in an embodiment of the present application. As shown in FIG41 , the encoding method includes the following steps 411 to 412:

[0347] Step 411: searching for a first co-located node of a current node in a first reference image based on geometric information of the current node in a current image, and searching for a second co-located node of the current node in a second reference image;

[0348] Step 412: Perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0349] In an embodiment of the present application, for the point cloud encoding method, when determining the attribute prediction value of the current node, it is based not only on the co-located node of the current node in the first reference image, but also on the co-located node of the current node in the second reference image; this is beneficial to improving the accuracy of inter-frame attribute prediction, thereby further compressing the temporal redundancy between images and saving point cloud code stream.

[0350] The following describes further optional implementations and related terms of each of the above steps.

[0351] In step 411 , based on the geometric information of the current node in the current image, a first co-located node of the current node is searched in the first reference image, and a second co-located node of the current node is searched in the second reference image.

[0352] It can be understood that the first co-located node and the second co-located node refer to nodes with the same geometric information / geometric coordinates as the current node. In an embodiment of the present application, the current image, the first reference image and the second reference image can be understood as different point cloud frames. In some embodiments, the arrangement structure of the point clouds of the current image, the first reference image and the second reference image is a RAHT attribute transform coding structure. The encoder can construct a RAHT attribute transform coding structure based on the geometric information of the node, that is, continuously merge nodes at the voxel level until the root node of the entire RAHT transform tree is obtained. For example, as shown in Figure 42, the first reference image is 421, the second reference image is 422, and the current image is 423. Assuming that the current node is 4231, its first co-located node and the second co-located node are the nodes indicated by the arrows in Figure 42.

[0353] In the embodiment of the present application, there is no limitation on the relationship between the first reference image and the second reference image and the current image. The first reference image and the second reference image can be two frames of images forward of the current image, or two frames of images backward. Alternatively, the first reference image can be the forward reference image of the current image, and the second reference image can be the backward reference image of the current image.

[0354] In step 412, inter-frame attribute prediction is performed on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0355] Optionally, in some embodiments (for the convenience of description, this embodiment is referred to as Embodiment 1), step 412 includes: when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

[0356] In some other embodiments (for ease of description, this embodiment is referred to as Embodiment 2), as shown in FIG43 , step 412 includes the following steps 4121 to 4123:

[0357] Step 4121: if the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, determine a first difference in the number of occupied child nodes between the first co-located node and the current node based on the placeholder information of the first co-located node and the current node; and

[0358] Step 4122: Determine a second difference in the number of occupied child nodes between the second co-located node and the current node based on the placeholder information of the second co-located node and the current node.

[0359] Step 4123: Determine the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number.

[0360] Furthermore, in some embodiments, step 4123 includes:

[0361] (1) when the first difference number is equal to the second difference number, determining the attribute prediction value of the current node according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node;

[0362] (2) when the first difference number is less than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node;

[0363] (3) When the first difference number is greater than the second difference number, the attribute prediction value of the current node is determined based on the attribute reconstruction value of the second co-located node.

[0364] Exemplarily, in some embodiments, when the first number of differences is smaller than the second number of differences, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node; when the first number of differences is greater than the second number of differences, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0365] It can be understood that the occupancy information of the first co-located node, the second co-located node and the current node all record the occupancy of their respective child nodes. The smaller the difference in the number of occupied child nodes between the current node and the co-located node, the stronger the geometric correlation between the corresponding two frames of point clouds / images. Correspondingly, the stronger the attribute correlation between the two frames of point clouds / images, the greater the temporal redundancy between the two. Therefore, when the first difference in the number of occupied child nodes between the first co-located node and the current node is less than the second difference in the number of occupied child nodes between the second co-located node and the current node, it means that there is greater temporal redundancy between the current image and the first reference image than between the current image and the second reference image. Therefore, in this case, the attribute prediction value of the current node can be determined based on the attribute reconstruction value of the first co-located node. For example, the attribute reconstruction value of the first co-located node can be directly used as the attribute prediction value of the current node. In this way, compared with determining the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node in this case, the temporal redundancy can be better compressed, thereby improving the encoding and decoding performance of the point cloud. Similarly, when the first difference number is greater than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node can better compress time redundancy and thus improve the encoding and decoding performance of the point cloud compared to determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node.

[0366] Regarding the "determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node" mentioned in both the above-mentioned embodiments 1 and 2, in some embodiments, it can be implemented as follows: based on the first weighting coefficient of the attribute reconstruction value of the first co-located node and the second weighting coefficient of the attribute reconstruction value of the second co-located node, the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node are weighted to obtain the attribute prediction value of the current node.

[0367] For example, in some embodiments, the first weighting coefficient is equal to the first value, and the second weighting coefficient is equal to the second value. That is, the first weighting coefficient and the second weighting coefficient are predefined values, which may be equal or unequal, but their sum is equal to 1.

[0368] In other embodiments, the first weighting coefficient may be determined based on the time interval between the acquisition of the current image and the first reference image; and / or the second weighting coefficient may be determined based on the time interval between the acquisition of the current image and the second reference image. For example, the longer the time interval, the larger the weighting coefficient. If the time interval between the acquisition of the current image and the first reference image is greater than the time interval between the acquisition of the current image and the second reference image, the first weighting coefficient is smaller than the second weighting coefficient.

[0369] Specifically, in some embodiments, a mapping table between acquisition time intervals and weighting coefficients can be predefined, so that the encoder can determine the first weighting coefficient and the second weighting coefficient by looking up the table. Of course, determining the weighting coefficients based on this table lookup method is not limited to this. In short, the corresponding weighting coefficient can be determined based on the acquisition time interval between two frames of images.

[0370] In some further embodiments, the encoder may also determine the first weighting coefficient and the second weighting coefficient as follows: determine the rate-distortion costs of multiple candidate weighting coefficient groups; wherein the candidate weighting coefficient groups include a first candidate weighting coefficient of the attribute reconstruction value of the first co-located node and a second candidate weighting coefficient of the attribute reconstruction value of the second co-located node; select a candidate weighting coefficient group with the smallest rate-distortion cost from the multiple candidate weighting coefficient groups; use the first candidate weighting coefficient in the candidate weighting coefficient group with the smallest rate-distortion cost as the first weighting coefficient, and use the second candidate weighting coefficient in the candidate weighting coefficient group with the smallest rate-distortion cost as the second weighting coefficient.

[0371] It is understood that, at the encoder, since the actual attribute value of the current node is known, the rate-distortion cost of the candidate weighting coefficient group can be determined. Accordingly, the method further includes: the encoder writing the first and second weighting coefficients obtained based on the rate-distortion cost into the bitstream, and the decoder obtaining the first and second weighting coefficients by parsing the bitstream.

[0372] The above describes a method for determining the attribute prediction value of the current node when both the first co-located node and the second co-located node exist. It is understandable that the first co-located node may not exist in the first reference image, and / or the second co-located node may not exist in the second reference image. In this case, how to perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node is described in the following embodiment, namely:

[0373] In some embodiments, the step 412 of performing inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node further includes:

[0374] (1) when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, determining the attribute prediction value of the current node according to the attribute reconstruction value of the second co-located node;

[0375] (2) When the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, determine the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node.

[0376] (3) When the first co-located node does not exist in the first reference image and the second co-located node does not exist in the second reference image, determine the attribute prediction value of the current node based on the attribute reconstructed value of at least one neighboring node of the current node in the current image. For example, the attribute prediction value of the current node is determined based on the attribute reconstructed value of the at least one neighboring node.

[0377] Exemplarily, in some embodiments, when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0378] Exemplarily, in some embodiments, when the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0379] In some embodiments, the encoding method further includes: determining an attribute residual value of the current node according to the attribute prediction value of the current node; and generating a code stream according to the attribute residual value of the current node.

[0380] For example, in some embodiments, the attribute prediction value of the current node is the AC coefficient prediction value of the current node, and the attribute residual value of the current node is the AC coefficient residual value of the current node. The encoder may determine the AC coefficient residual value of the current node based on the AC coefficient prediction value and the actual AC coefficient value of the current node.

[0381] The embodiment of the present application provides a decoding method. FIG44 is a schematic diagram of an implementation flow of the decoding method provided in the embodiment of the present application. As shown in FIG44 , the decoding method includes the following steps 441 to 442:

[0382] Step 441 , searching for a first co-located node of a current node in a first reference image based on geometric information of the current node in a current image, and searching for a second co-located node of the current node in a second reference image;

[0383] Step 442: Perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

[0384] In an embodiment of the present application, a decoding method for point clouds adopts an inter-frame attribute prediction method similar to that of the encoding end, that is, when determining the attribute prediction value of the current node, it is based not only on the co-located node of the current node in the first reference image, but also on the co-located node of the current node in the second reference image; this is beneficial to improving the accuracy of inter-frame attribute prediction, and thus is beneficial to restoring higher quality point cloud data.

[0385] In some embodiments, the inter-frame attribute prediction of the current node is performed based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node, including: when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is determined based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

[0386] In some embodiments, the inter-frame attribute prediction of the current node is performed based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node, including: when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, determining the first difference number of occupied sub-nodes between the first co-located node and the current node based on the occupancy information of the first co-located node and the current node; and determining the second difference number of occupied sub-nodes between the second co-located node and the current node based on the occupancy information of the second co-located node and the current node; and determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number.

[0387] Furthermore, in some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes at least one of the following:

[0388] (1) when the first difference number is equal to the second difference number, determining the attribute prediction value of the current node according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node;

[0389] (2) when the first difference number is less than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node;

[0390] (3) When the first difference number is greater than the second difference number, the attribute prediction value of the current node is determined based on the attribute reconstruction value of the second co-located node.

[0391] Exemplarily, in some embodiments, when the first difference number is smaller than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0392] Exemplarily, in some embodiments, when the first difference number is greater than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0393] In some embodiments, performing inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node further includes at least one of the following:

[0394] (1) When the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, determining the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node.

[0395] (2) When the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, determine the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node.

[0396] (3) When the first co-located node does not exist in the first reference image and the second co-located node does not exist in the second reference image, determine the attribute prediction value of the current node based on the attribute reconstruction value of at least one neighboring node of the current node in the current image.

[0397] Exemplarily, in some embodiments, when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0398] Exemplarily, in some embodiments, when the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0399] In some embodiments, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node includes: weighting the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node based on a first weighting coefficient of the attribute reconstruction value of the first co-located node and a second weighting coefficient of the attribute reconstruction value of the second co-located node to obtain the attribute prediction value of the current node.

[0400] Exemplarily, in some embodiments, the first weighting coefficient is equal to a first value, and the second weighting coefficient is equal to a second value.

[0401] Exemplarily, in some other embodiments, the decoder may determine the first weighting coefficient based on the interval between the acquisition time of the current image and the first reference image; and / or determine the second weighting coefficient based on the interval between the acquisition time of the current image and the second reference image.

[0402] Illustratively, in some further embodiments, the decoder may obtain the first weighting coefficient and the second weighting coefficient by parsing a bitstream.

[0403] In the RAHT inter-frame prediction transform coding scheme, the aforementioned attribute prediction value refers to the AC coefficient prediction value, and the aforementioned attribute reconstruction value refers to the AC coefficient reconstruction value. Based on this, in some embodiments, the decoding method further includes: parsing the bitstream to obtain the AC coefficient residual value of the current node; determining the AC coefficient reconstruction value of the current node based on the AC coefficient residual value of the current node and the AC coefficient prediction value; and performing a RAHT inverse transform on the AC coefficient reconstruction value of the current node to obtain the attribute reconstruction value of the current node, where the attribute reconstruction value obtained by the RAHT inverse transform is not the AC coefficient reconstruction value.

[0404] It should be noted that in the embodiments of the present application, the method for determining the attribute prediction value of the current node in the decoding method is the same as the method for determining the attribute prediction value of the current node in the encoding method. Therefore, for technical details not disclosed in the decoding method embodiment, please refer to the description of the encoding method embodiment of the present application for understanding.

[0405] The following describes an exemplary application of the embodiments of the present application in a practical application scenario.

[0406] In the embodiment of the present application, the RAHT attribute coding layer is first defined. The attribute RAHT transform coding order is to divide the attribute from the root node in sequence until it is divided into the voxel level (1x1x1), thereby completing the encoding and attribute reconstruction of the entire point cloud attribute. In some embodiments, as shown in Figure 45, the layer obtained by downsampling once along the Z direction, Y direction, and X direction is defined as a RAHT transform layer, that is, layer; secondly, based on the RAHT attribute coding layer, a bidirectional predictive coding scheme is introduced. The specific algorithm is shown in Figure 42:

[0407] First, when encoding / decoding the node attributes of the current layer, the number of nodes to be encoded / decoded and the position of each node can be obtained. Second, for each node to be encoded / decoded (i.e., the current node), the current node position is used to search for the number of 19 neighboring nodes adjacent to the spatial position of the current node to be encoded / decoded. Based on the attribute reconstruction values ​​of these 19 neighboring nodes, the corresponding intra-frame prediction value is obtained. Based on the spatial position of the current node to be encoded, the same-position node is searched in the reference frame. The specific encoding-side algorithm is described in 1-4 below:

[0408] 1. Use the position of the current node to be coded to search in the cache of the forward reference frame whether there is a co-located node of the current node to be coded in the corresponding prediction layer of the reference frame. If so, assume that the predicted value of the predicted node of the forward reference frame is predVal1; based on the same algorithm, determine whether there is a co-located node of the current node in the backward reference frame. If so, assume that the predicted value of the predicted node of the backward reference frame is predVal2. The final predicted value predVal of the AC coefficient attribute of the current node is: predVal=w1*predVal1+w2*predVal2

[0409] Among them, w1 is the prediction weight of the forward reference frame, and w2 is the prediction weight of the backward reference frame. The position of the current node to be encoded can be understood as the geometric information of the current node, the forward reference frame can be understood as the first reference image, the backward reference frame can be understood as the second reference image, predVal1 can be understood as the AC coefficient reconstruction value / AC coefficient attribute reconstruction value of the first co-located node, predVal2 can be understood as the AC coefficient reconstruction value / AC coefficient attribute reconstruction value of the second co-located node, and the AC coefficient attribute prediction value can also be called the AC coefficient prediction value.

[0410] 2. Otherwise, if the same node in the forward reference frame exists, the AC coefficient attribute prediction value predVal of the current node is: predVal = predVal1

[0411] 3. Otherwise, if the co-located node of the backward reference frame exists, the AC coefficient attribute prediction value predVal of the current node is: predVal = predVal2

[0412] 4. Otherwise, if the co-located node of the backward reference frame of the current node does not exist, the AC coefficient attribute prediction value of the current node is the intra-frame prediction value.

[0413] Based on the above algorithm, the AC coefficient attributes of the N child nodes of the current node are predicted. It should be noted here that in inter-frame prediction coding, the position corresponding to the co-located node is first obtained, and then the AC coefficient attributes of the M child nodes in the co-located node or the reference frame are used. Secondly, when predicting the AC coefficient of each child node, if the AC coefficient attribute value of the child node between the corresponding frames is not zero, the AC coefficient prediction value of the corresponding child node is the inter-frame prediction; otherwise, the AC coefficient attribute prediction value corresponding to the current child node to be coded is the intra-frame prediction value.

[0414] The specific algorithm of the decoding end is described in 1-4 below:

[0415] 1. Use the position of the current node to be coded to search in the cache of the forward reference frame to see if there is a co-located node of the current node to be coded in the corresponding prediction layer of the reference frame. If so, assume that the predicted value of the predicted node of the forward reference frame is predVal1; based on the same algorithm, determine whether there is a co-located node of the current node in the backward reference frame. If so, assume that the predicted value of the backward reference frame is predVal2, then the final AC coefficient attribute prediction value predVal of the current node is: predVal=w1*predVal1+w2*predVal2

[0416] Among them, w1 is the prediction weight of the forward reference frame, and w2 is the prediction weight of the backward reference frame.

[0417] 2. Otherwise, if the same node in the forward reference frame exists, the AC coefficient attribute prediction value of the current node is: predVal = predVal1

[0418] 3. Otherwise, if the co-located node of the backward reference frame exists, the AC coefficient attribute prediction value of the current node is: predVal = predVal2

[0419] 4. Otherwise, if the co-located node of the backward reference frame of the current node does not exist, the AC coefficient attribute prediction value of the current node is the intra-frame prediction value.

[0420] An algorithm similar to that used on the encoder side obtains the AC coefficient prediction values ​​corresponding to the N child nodes of the current node to be decoded. Finally, the AC coefficient attribute prediction residuals of each child node are obtained from the bitstream. These prediction residuals are dequantized to obtain the reconstructed prediction residual values. The reconstructed prediction residual values ​​are then added to the predicted values ​​to reconstruct the reconstructed AC coefficient attribute value of the current child node. Finally, the attribute value of the point is recovered using the RAHT-based inverse attribute transform.

[0421] In the above scheme, for each node to be encoded, a bidirectional prediction coding algorithm is introduced. When the same-position nodes of the forward and backward reference frames exist at the same time, the prediction weights of the forward reference frame and the backward reference frame are obtained according to the time slot intervals between the forward and backward reference frames and the current frame to be encoded. Here, only the time slot relationship of the sequence set is considered, and the distribution of the AC coefficient attributes of the node to be encoded is not considered. This scheme optimizes the prediction weights of the forward and backward reference frames. Specifically: for the attributes of each layer to be encoded, the rate-distortion optimization algorithm is used at the encoding end to obtain the optimal prediction weight value of the current layer to be encoded. Then, the prediction weight value is passed to the decoding end. The decoding end uses the corresponding prediction weight and the predicted attribute values ​​of the adjacent references to reconstruct and restore the attribute reconstruction value of the node to be decoded, thereby further improving the attribute coding efficiency of the point cloud.

[0422] In an embodiment of the present application, when inter-frame RAHT prediction is performed on an attribute, if the current layer to be coded can perform attribute prediction, a bidirectional prediction coding algorithm is introduced based on the RAHT attribute coding structure. For each node to be coded, the corresponding co-located node is obtained in the forward and backward reference frames respectively by the spatial position of the node to be coded. Then, the co-located node is used to obtain the AC coefficient attribute prediction value of the current node to be coded. Based on such an algorithm, the AC coefficient attributes of the forward and backward reference frames can be comprehensively considered, so that the time slot redundancy characteristics between the forward and backward adjacent frames can be better removed, thereby further improving the point cloud attribute coding efficiency. As shown in Table 2, the coding efficiency of the attributes is demonstrated. As shown in Table 2, it can be seen that after the introduction of the RAHT bidirectional inter-frame prediction coding algorithm, for the sequence using inter-frame prediction coding attributes, the BPP of the attribute coding is reduced by about 1.75%, significantly improving the coding efficiency of the point cloud attributes.

[0423] Table 2

[0424] In an embodiment of the present application, when performing RAHT predictive coding on attributes, inter-frame predictive coding is performed on the attributes of each node, introducing a RAHT bidirectional predictive coding structure. For each node to be coded, the spatial position of the node to be coded is used to obtain the corresponding co-located node in the forward reference frame and the backward reference frame. Then, based on the different situations of the forward reference frame and the backward reference frame, the attributes of the current node to be coded are inter-frame predictive coded. Finally, the decoding end obtains the attribute prediction value of the corresponding node based on the same algorithm, and uses the corresponding node attribute prediction value and the attribute prediction residual to recover the attribute reconstruction value of the current node to be decoded. In this scheme, the focus is on introducing a bidirectional inter-frame prediction coding algorithm when encoding or decoding the attributes of each node of each RAHT code. By referring to the reconstructed attribute values ​​of the forward reference node and the backward reference node, the redundant characteristics of the attributes between adjacent frames can be further removed. The algorithm does not restrict the prediction weights of the forward and backward reference nodes. For example, the inter-frame prediction weights of different prediction nodes can be determined according to the time slot intervals of the forward and backward reference frames, or the weights of the forward and backward reference nodes of the current node can be adaptively obtained according to the spatial position of each node and the attribute distribution of the prediction nodes.

[0425] (1) This scheme can further modify the attribute bidirectional inter-frame prediction mode.

[0426] In the above scheme, the forward and backward reference nodes are obtained by using the node to be coded, and then the inter-frame attribute prediction value of the current node to be coded is obtained according to certain conditions. In this scheme, the inter-frame attribute prediction value of the prediction node is further optimized, specifically as follows: assuming that the occupancy information of the current node to be coded is occupancy, the corresponding prediction node is obtained in the forward reference frame using the spatial position of the current node to be coded, assuming that the occupancy information of the prediction node is prevOccupancy, and based on the same algorithm, the corresponding prediction node is obtained in the forward reference frame, assuming that the AC coefficient reconstruction value of the prediction sub-node stored in the forward reference frame is predVal1, assuming that the occupancy information of the prediction node is backOccupancy, and assuming that the AC coefficient reconstruction value of the prediction sub-node stored in the backward reference frame is predVal2, then the prediction value of the current node is:

[0427] 1. If both co-located prediction nodes exist, determine the number of differences between the occupancy information of the current node to be coded and the forward prediction node. Assume that this is N1 (i.e., the first difference number), and the number of differences between the occupancy information of the backward reference frame and the current node to be coded is N2 (i.e., the second difference number). Then:

[0428] 1) When N1 is less than N2, the AC coefficient attribute prediction value predVal of the current node is: predVal = predVal1

[0429] 2) When N1 is greater than N2, the AC coefficient attribute prediction value predVal of the current node is: predVal = predVal2

[0430] 3) When N1 is equal to N2, the AC coefficient attribute prediction value predVal of the current node is: predVal=w1*predVal1+w2*predVal2

[0431] w1 is the prediction weight of the forward reference frame, and w2 is the prediction weight of the backward reference frame.

[0432] 2. Otherwise, if the same node in the forward reference frame exists, the AC coefficient attribute prediction value predVal of the current node is: predVal = predVal1

[0433] 3. Otherwise, if the co-located node of the backward reference frame exists, the AC coefficient attribute prediction value predVal of the current node is: predVal = predVal2

[0434] 4. Otherwise, if the co-located node of the backward reference frame of the current node does not exist, the AC coefficient attribute prediction value of the current node is the intra-frame prediction value.

[0435] It should be noted that although the steps of the method of the present application are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all steps must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps; or steps in different embodiments may be combined to form a new technical solution.

[0436] An embodiment of the present application provides a decoder. Figure 46 is a structural diagram of the decoder provided by the embodiment of the present application. As shown in Figure 46, the decoder 46 includes: a first search module 461, configured to search for the first co-located node of the current node in the first reference image and the second co-located node of the current node in the second reference image based on the geometric information of the current node of the current image; a first prediction module 462, configured to perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node.

[0437] In some embodiments, the first prediction module 462 is configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image.

[0438] In some embodiments, the first prediction module 462 is configured to: determine a first difference number of occupied child nodes between the first co-located node and the current node based on the placeholder information of the first co-located node and the current node when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image; and determine a second difference number of occupied child nodes between the second co-located node and the current node based on the placeholder information of the second co-located node and the current node; and determine an attribute prediction value of the current node based on the relationship between the first difference number and the second difference number.

[0439] In some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes: when the first difference number is equal to the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

[0440] In some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes: when the first difference number is less than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node.

[0441] In some embodiments, when the first difference number is smaller than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0442] In some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes: when the first difference number is greater than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node.

[0443] In some embodiments, when the first difference number is greater than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0444] In some embodiments, the first prediction module 462 is further configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image.

[0445] In some embodiments, when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0446] In some embodiments, the first prediction module 462 is further configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node when the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image.

[0447] In some embodiments, when the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0448] In some embodiments, the first prediction module 462 is further configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of at least one neighboring node of the current node in the current image when the first co-located node does not exist in the first reference image and the second co-located node does not exist in the second reference image.

[0449] In some embodiments, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node includes: weighting the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node based on a first weighting coefficient of the attribute reconstruction value of the first co-located node and a second weighting coefficient of the attribute reconstruction value of the second co-located node to obtain the attribute prediction value of the current node.

[0450] In some embodiments, the first weighting coefficient is equal to a first value, and the second weighting coefficient is equal to a second value.

[0451] In some embodiments, the first prediction module 462 is further configured to determine the first weighting coefficient according to the interval between the acquisition time of the current image and the first reference image.

[0452] In some embodiments, the first prediction module 462 is further configured to determine the second weighting coefficient according to the interval between the acquisition times of the current image and the second reference image.

[0453] In some embodiments, the decoder 46 further includes a parsing module, and the parsing module is configured to parse the code stream to obtain the first weighting coefficient and the second weighting coefficient.

[0454] In some embodiments, the attribute prediction value of the current node is the AC coefficient prediction value of the current node; the decoder 46 also includes a parsing module, and the parsing module is configured to: parse the code stream to obtain the AC coefficient residual value of the current node; determine the AC coefficient reconstruction value of the current node based on the AC coefficient residual value of the current node and the AC coefficient prediction value; perform RAHT inverse transform on the AC coefficient reconstruction value of the current node to obtain the attribute reconstruction value of the current node.

[0455] The description of the above decoder embodiment is similar to the description of the above encoding / decoding method embodiment, and has similar beneficial effects as the encoding / decoding method embodiment. For technical details not disclosed in the decoder embodiment of this application, please refer to the description of the encoding / decoding method embodiment of this application for understanding.

[0456] An embodiment of the present application provides an encoder, and Figure 47 is a structural diagram of the encoder provided by the embodiment of the present application. As shown in Figure 47, the encoder 47 includes: a second search module 471, configured to search for the first co-located node of the current node in the first reference image and the second co-located node of the current node in the second reference image based on the geometric information of the current node of the current image; a second prediction module 472, configured to perform inter-frame attribute prediction on the current node based on the first co-located node and the second co-located node to obtain the attribute prediction value of the current node.

[0457] In some embodiments, the second prediction module 472 is configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image.

[0458] In some embodiments, the second prediction module 472 is configured to: determine a first difference number of occupied child nodes between the first co-located node and the current node based on the placeholder information of the first co-located node and the current node when the first co-located node exists in the first reference image and the second co-located node exists in the second reference image; and determine a second difference number of occupied child nodes between the second co-located node and the current node based on the placeholder information of the second co-located node and the current node; and determine the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number.

[0459] In some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes: when the first difference number is equal to the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

[0460] In some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes: when the first difference number is less than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node.

[0461] In some embodiments, when the first difference number is smaller than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0462] In some embodiments, determining the attribute prediction value of the current node based on the relationship between the first difference number and the second difference number includes: when the first difference number is greater than the second difference number, determining the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node.

[0463] In some embodiments, when the first difference number is greater than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0464] In some embodiments, the second prediction module 472 is further configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of the second co-located node when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image.

[0465] In some embodiments, when the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

[0466] In some embodiments, the second prediction module 472 is further configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node when the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image.

[0467] In some embodiments, when the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

[0468] In some embodiments, the second prediction module 472 is further configured to: determine the attribute prediction value of the current node based on the attribute reconstruction value of at least one neighboring node of the current node in the current image when the first co-located node does not exist in the first reference image and the second co-located node does not exist in the second reference image.

[0469] In some embodiments, determining the attribute prediction value of the current node based on the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node includes: weighting the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node based on a first weighting coefficient of the attribute reconstruction value of the first co-located node and a second weighting coefficient of the attribute reconstruction value of the second co-located node to obtain the attribute prediction value of the current node.

[0470] In some embodiments, the first weighting coefficient is equal to a first value, and the second weighting coefficient is equal to a second value.

[0471] In some embodiments, the second prediction module 472 is further configured to determine the first weighting coefficient according to the interval between the acquisition time of the current image and the first reference image.

[0472] In some embodiments, the second prediction module 472 is further configured to determine the second weighting coefficient according to the interval between the acquisition times of the current image and the second reference image.

[0473] In some embodiments, the second prediction module 472 is further configured to: determine the rate-distortion costs of multiple candidate weighting coefficient groups; wherein the candidate weighting coefficient groups include a first candidate weighting coefficient of the attribute reconstruction value of the first co-located node and a second candidate weighting coefficient of the attribute reconstruction value of the second co-located node; select a candidate weighting coefficient group with the smallest rate-distortion cost from the multiple candidate weighting coefficient groups; use the first candidate weighting coefficient in the candidate weighting coefficient group with the smallest rate-distortion cost as the first weighting coefficient, and use the second candidate weighting coefficient in the candidate weighting coefficient group with the smallest rate-distortion cost as the second weighting coefficient.

[0474] In some embodiments, the encoder 47 further includes an encoding module configured to write the first weighting coefficient and the second weighting coefficient into a bitstream.

[0475] In some embodiments, the encoder 47 further includes an encoding module, which is configured to: determine the attribute residual value of the current node according to the attribute prediction value of the current node; and generate a code stream according to the attribute residual value of the current node.

[0476] In some embodiments, the attribute prediction value of the current node is the AC coefficient prediction value of the current node, and the attribute residual value of the current node is the AC coefficient residual value of the current node.

[0477] The description of the above encoder embodiment is similar to the description of the above encoding method embodiment, and has similar beneficial effects as the encoding method embodiment. For technical details not disclosed in the encoder embodiment of this application, please refer to the description of the encoding method embodiment of this application for understanding.

[0478] It should be noted that the division of modules by the encoder / decoder described in the embodiments of the present application is schematic and is only a logical functional division. In actual implementation, other division methods may be used. In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or can exist physically separately, or two or more units can be integrated into a single unit. The above-mentioned integrated units can be implemented in the form of hardware or software functional units. It can also be implemented in the form of a combination of software and hardware.

[0479] It should be noted that, in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device to execute all or part of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0480] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the encoding method or the decoding method as described in the embodiment of the present application is implemented.

[0481] An embodiment of the present application provides a decoder. As shown in FIG48 , the decoder 48 includes: a first communication interface 481, a first memory 482, and a first processor 483; the various components are coupled together via a first bus system 484. It is understandable that the first bus system 484 is used to implement connection and communication between these components. In addition to the data bus, the first bus system 484 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, various buses are labeled as the first bus system 484 in FIG48. Among them, the first communication interface 481 is used to receive and send signals in the process of sending and receiving information between other external network elements; the first memory 482 is used to store a computer program that can be run on the first processor 483; and the first processor 483 is used to execute the encoding method described in the embodiment of the present application when running the computer program.

[0482] It is understood that the first memory 482 in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The first memory 482 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0483] The first processor 483 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits or software instructions in the first processor 483. The above-mentioned first processor 483 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the first memory 482 , and the first processor 483 reads the information in the first memory 482 and completes the steps of the above method in combination with its hardware.

[0484] It is to be understood that these embodiments described in the present application can be implemented with hardware, software, firmware, middleware, microcode or its combination.For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (Application Specific Integrated Circuits, ASIC), digital signal processor (Digital Signal Processing, DSP), digital signal processing equipment (DSP Device, DSPD), programmable logic device (Programmable Logic Device, PLD), field programmable gate array (Field-Programmable Gate Array, FPGA), general-purpose processor, controller, microcontroller, microprocessor, other electronic units for performing functions described in the present application or its combination.For software implementation, the technology described in the present application can be realized by the module (such as process, function etc.) that performs functions described in the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0485] Optionally, as another embodiment, the first processor 483 is further configured to execute any of the aforementioned encoding method embodiments when running the computer program.

[0486] The present application implements an encoder, as shown in Figure 49, the encoder 49 includes: a second communication interface 491, a second memory 492 and a second processor 493; the various components are coupled together through a second bus system 494. It can be understood that the second bus system 494 is used to realize the connection and communication between these components. In addition to the data bus, the second bus system 494 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, various buses are labeled as the second bus system 494 in Figure 49. Among them, the second communication interface 491 is used to receive and send signals in the process of sending and receiving information between other external network elements; the second memory 492 is used to store a computer program that can be run on the second processor 493; the second processor 493 is used to execute the decoding method described in the embodiment of the present application when running the computer program.

[0487] It can be understood that the hardware functions of the second memory 492 and the first memory 482 are similar, and the hardware functions of the second processor 493 and the first processor 483 are similar; they will not be described in detail here.

[0488] The embodiment of the present application further provides a code stream, which is obtained by using the aforementioned encoding method.

[0489] An embodiment of the present application provides an electronic device, comprising: a processor adapted to execute a computer program; and a computer-readable storage medium storing the computer program, wherein when the computer program is executed by the processor, the encoding method and / or decoding method described in the embodiment of the present application are implemented. The electronic device can be any type of device capable of video encoding and / or video decoding, such as a mobile phone, tablet computer, laptop computer, personal computer, television, projection device, or monitoring device.

[0490] It should be noted that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium, and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0491] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description of the various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other. For the sake of brevity, they will not be repeated here.

[0492] The term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, object A and / or object B can mean: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0493] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0494] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0495] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed across multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of this embodiment.

[0496] In addition, all functional modules in the embodiments of the present application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0497] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), magnetic disks or optical disks. Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. And the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0498] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined, if they do not conflict, to obtain new method embodiments. The features disclosed in the several product embodiments provided in this application can be arbitrarily combined, if they do not conflict, to obtain new product embodiments. The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined, if they do not conflict, to obtain new method embodiments or device embodiments.

[0499] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A decoding method, applied to a decoder, the method comprising: According to geometric information of a current node in a current image, searching for a first co-located node of the current node in a first reference image, and searching for a second co-located node of the current node in a second reference image; According to the first co-located node and the second co-located node, inter-frame attribute prediction is performed on the current node to obtain an attribute prediction value of the current node.

2. The method according to claim 1, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

3. The method according to claim 1, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, determining a first difference number of occupied child nodes between the first co-located node and the current node according to the placeholder information of the first co-located node and the current node; and Determine, according to the placeholder information of the second co-located node and the current node, a second difference number of occupied child nodes between the second co-located node and the current node; The attribute prediction value of the current node is determined according to the relationship between the first difference number and the second difference number.

4. The method according to claim 3, wherein: The determining the attribute prediction value of the current node according to the relationship between the first difference number and the second difference number includes: When the first difference number is equal to the second difference number, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

5. The method according to claim 3, wherein: The determining the attribute prediction value of the current node according to the relationship between the first difference number and the second difference number includes: When the first difference number is smaller than the second difference number, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node.

6. The method according to claim 5, wherein: In a case where the first difference number is smaller than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

7. The method according to claim 3, wherein: The determining the attribute prediction value of the current node according to the relationship between the first difference number and the second difference number includes: When the first difference number is greater than the second difference number, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the second co-located node.

8. The method according to claim 7, wherein: In the case where the first difference number is greater than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

9. The method according to any one of claims 1 to 8, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the second co-located node.

10. The method according to claim 9, wherein: In a case where the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

11. The method according to any one of claims 1 to 8, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node.

12. The method according to claim 11, wherein: The first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image. If present, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

13. The method according to any one of claims 1 to 8, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: When the first co-located node does not exist in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is determined based on the attribute reconstruction value of at least one neighboring node of the current node in the current image.

14. The method according to claim 2 or 4, wherein: The determining the attribute prediction value of the current node according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node includes: The attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node are weighted according to a first weighting coefficient of the attribute reconstruction value of the first co-located node and a second weighting coefficient of the attribute reconstruction value of the second co-located node to obtain an attribute prediction value of the current node.

15. The method according to claim 14, wherein: The first weighting coefficient is equal to a first value, and the second weighting coefficient is equal to a second value.

16. The method according to claim 14, wherein: The method further comprises: The first weighting coefficient is determined according to an interval between acquisition times of the current image and the first reference image.

17. The method according to claim 14, wherein: The method further comprises: The second weighting coefficient is determined according to the interval between the acquisition times of the current image and the second reference image.

18. The method according to claim 14, wherein: The method further comprises: Parse the code stream to obtain the first weighting coefficient and the second weighting coefficient.

19. The method according to claim 1, wherein: The attribute prediction value of the current node is the AC coefficient prediction value of the current node; The method further comprises: Parse the bitstream to obtain the AC coefficient residual value of the current node; Determine an AC coefficient reconstruction value of the current node according to the AC coefficient residual value of the current node and the AC coefficient prediction value; Performing a RAHT inverse transform on the AC coefficient reconstruction value of the current node to obtain an attribute reconstruction value of the current node.

20. A coding method, applied to an encoder, the method comprising: According to geometric information of a current node in a current image, searching for a first co-located node of the current node in a first reference image, and searching for a second co-located node of the current node in a second reference image; According to the first co-located node and the second co-located node, inter-frame attribute prediction is performed on the current node to obtain an attribute prediction value of the current node.

21. The method according to claim 20, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

22. The method according to claim 20, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node exists in the first reference image and the second co-located node exists in the second reference image, determining a first difference number of occupied child nodes between the first co-located node and the current node according to the placeholder information of the first co-located node and the current node; and Determine, according to the placeholder information of the second co-located node and the current node, a second difference number of occupied child nodes between the second co-located node and the current node; The attribute prediction value of the current node is determined according to the relationship between the first difference number and the second difference number.

23. The method according to claim 22, wherein: The determining the attribute prediction value of the current node according to the relationship between the first difference number and the second difference number includes: When the first difference number is equal to the second difference number, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node.

24. The method according to claim 22, wherein: The determining the attribute prediction value of the current node according to the relationship between the first difference number and the second difference number includes: When the first difference number is smaller than the second difference number, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node.

25. The method according to claim 24, wherein: When the first difference number is less than the second difference number, the attribute prediction value of the current node is equal to the first node. Rebuilds the value of a bit node's attribute.

26. The method of claim 22, wherein: The determining the attribute prediction value of the current node according to the relationship between the first difference number and the second difference number includes: When the first difference number is greater than the second difference number, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the second co-located node.

27. The method according to claim 26, wherein: In the case where the first difference number is greater than the second difference number, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

28. The method according to any one of claims 20 to 27, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the second co-located node.

29. The method according to claim 28, wherein: In a case where the first co-located node does not exist in the first reference image and the second co-located node exists in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the second co-located node.

30. The method according to any one of claims 20 to 27, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: In a case where the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is determined according to the attribute reconstruction value of the first co-located node.

31. The method according to claim 30, wherein: In a case where the first co-located node exists in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is equal to the attribute reconstruction value of the first co-located node.

32. The method according to any one of claims 20 to 27, wherein: The performing inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node includes: When the first co-located node does not exist in the first reference image and the second co-located node does not exist in the second reference image, the attribute prediction value of the current node is determined based on the attribute reconstruction value of at least one neighboring node of the current node in the current image.

33. The method according to claim 21 or 23, wherein: The determining the attribute prediction value of the current node according to the attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node includes: The attribute reconstruction value of the first co-located node and the attribute reconstruction value of the second co-located node are weighted according to a first weighting coefficient of the attribute reconstruction value of the first co-located node and a second weighting coefficient of the attribute reconstruction value of the second co-located node to obtain an attribute prediction value of the current node.

34. The method of claim 33, wherein: The first weighting coefficient is equal to a first value, and the second weighting coefficient is equal to a second value.

35. The method of claim 33, wherein: The method further comprises: The first weighting coefficient is determined according to an interval between acquisition times of the current image and the first reference image.

36. The method of claim 33, wherein: The method further comprises: The second weighting coefficient is determined according to the interval between the acquisition times of the current image and the second reference image.

37. The method of claim 33, wherein: The method further comprises: Determining rate-distortion costs of a plurality of candidate weighting coefficient groups; wherein the candidate weighting coefficient groups include a first candidate weighting coefficient of the attribute reconstruction value of the first co-located node and a second candidate weighting coefficient of the attribute reconstruction value of the second co-located node; Selecting a candidate weighting coefficient group with the smallest rate-distortion cost from the plurality of candidate weighting coefficient groups; A first candidate weighting coefficient in the candidate weighting coefficient group with the minimum rate-distortion cost is used as the first weighting coefficient, and a second candidate weighting coefficient in the candidate weighting coefficient group with the minimum rate-distortion cost is used as the second weighting coefficient.

38. The method of claim 37, wherein: The method further comprises: The first weighting coefficient and the second weighting coefficient are written into a bit stream.

39. The method according to any one of claims 20 to 38, wherein: The method further comprises: Determining an attribute residual value of the current node according to the attribute prediction value of the current node; Generate a code stream according to the attribute residual value of the current node.

40. The method of claim 39, wherein: The attribute prediction value of the current node is the AC coefficient prediction value of the current node, and the attribute residual value of the current node is the AC coefficient residual value of the current node.

41. A decoder, the decoder comprising: A first search module is configured to search for a first co-located node of a current node in a first reference image and search for a second co-located node of the current node in a second reference image according to geometric information of the current node in the current image; The first prediction module is configured to perform inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

42. A decoder, comprising a first memory and a first processor; wherein: The first memory is used to store a computer program that can be run on the first processor; The first processor is configured to execute the method according to any one of claims 1 to 19 when running the computer program.

43. An encoder, comprising: A second search module is configured to search for a first co-located node of a current node in a first reference image and search for a second co-located node of the current node in a second reference image according to geometric information of the current node in the current image; The second prediction module is configured to perform inter-frame attribute prediction on the current node according to the first co-located node and the second co-located node to obtain an attribute prediction value of the current node.

44. An encoder, comprising a second memory and a second processor; wherein: The second memory is used to store a computer program that can be run on the second processor; The second processor is configured to execute the method according to any one of claims 20 to 40 when running the computer program.

45. A code stream, wherein the code stream is obtained by using the encoding method according to any one of claims 38 to 40.

46. ​​A computer-readable storage medium, wherein: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 19 is implemented, or the method according to any one of claims 20 to 40 is implemented.

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