Coding and decoding method, codec and storage medium

Abstract

The embodiment of the invention provides a coding and decoding method, a codec and a storage medium. The decoding method comprises the following steps; the code stream is analyzed, first information is determined, the first information is used for indicating a target decoding mode from multiple decoding modes corresponding to a current layer, and the multiple decoding modes comprise RAHT conversion decoding and RAHT prediction combined conversion decoding; and decoding the attribute information of the nodes of the current layer according to the target decoding mode.

Description

Coding and decoding method, codec and storage medium Technical Field

[0001] The present application relates to the field of point cloud encoding and decoding technology, and in particular to an encoding and decoding method, a codec, and a storage medium. Background Art

[0002] In the process of encoding point cloud attribute information, if region adaptive hierarchical transform (RAHT) inter-frame prediction is enabled, the related technology will select the best RAHT prediction mode among RAHT inter-frame prediction and RAHT intra-frame prediction.

[0003] However, in some cases, the prediction performance of RAHT inter-frame prediction and RAHT intra-frame prediction is relatively poor. In this case, if the solution provided by the relevant technology is adopted, the encoding performance of the point cloud attribute information will be poor.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide a coding and decoding method, a codec, and a storage medium to improve the coding performance of point cloud attribute information. The following describes various aspects of the present application.

[0006] In a first aspect, a decoding method is provided, which is applied to a decoder, including: parsing a bitstream to determine first information, where the first information is used to indicate a target decoding mode from multiple decoding modes corresponding to a current layer, the multiple decoding modes including RAHT transform decoding and RAHT prediction combined with transform decoding; and decoding attribute information of a node of the current layer according to the target decoding mode.

[0007] In a second aspect, a coding method is provided, which is applied to an encoder, including: determining a target coding mode corresponding to a current layer from a plurality of coding modes, the plurality of coding modes including RAHT transform coding and RAHT prediction combined with transform coding; encoding attribute information of the nodes of the current layer according to the target coding mode.

[0008] According to a third aspect, a decoder is provided, comprising: a first decoding unit configured to parse a code stream and determine first information, wherein the first information is used to indicate a target decoding mode from a plurality of decoding modes corresponding to a current layer, the plurality of decoding modes including RAHT transform decoding and RAHT prediction combined with transform decoding; and a second decoding unit configured to decode attribute information of a node of the current layer according to the target decoding mode.

[0009] According to a fourth aspect, a decoder is provided, comprising: a memory for storing a computer program; and a processor for executing the method according to the first aspect when running the computer program.

[0010] In a fifth aspect, an encoder is provided, comprising: a first determination unit, configured to determine a target coding mode corresponding to a current layer from a plurality of coding modes, the plurality of coding modes including RAHT transform coding and RAHT prediction combined with transform coding; a first coding unit, configured to encode attribute information of the nodes of the current layer according to the target coding mode.

[0011] In a sixth aspect, an encoder is provided, comprising: a memory for storing a computer program; and a processor for executing the method described in the second aspect when running the computer program.

[0012] In a seventh aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the method as described in the first aspect or the second aspect is implemented.

[0013] In an eighth aspect, a non-volatile computer-readable storage medium for storing a bit stream is provided, wherein the bit stream is generated by an encoding method using an encoder, or the bit stream is decoded by a decoding method using a decoder, wherein the decoding method is the method described in the first aspect, and the encoding method is the method described in the second aspect.

[0014] In the related art, after starting RAHT prediction combined with transform coding, RAHT prediction needs to be performed on the basis of RAHT transform. The RAHT prediction process is relatively complicated. If the RAHT prediction performance is poor, forcing RAHT prediction will not significantly improve the coding efficiency of point cloud attribute information. At this time, the existence of the RAHT prediction process will lead to an increase in coding complexity and introduce more additional coding losses. It is precisely in view of the above problems that the embodiment of the present application proposes to select a coding mode between RAHT prediction combined with transform coding and RAHT transform coding. That is to say, in the embodiment of the present application, even if RAHT prediction combined with transform coding is started, it is possible to revert to RAHT transform coding. Therefore, when the RAHT prediction performance is poor, a scheme with lower coding complexity and smaller coding loss can be selected, thereby optimizing the coding performance as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] FIG1A is a schematic diagram of a three-dimensional point cloud image.

[0016] FIG1B is a partially enlarged view of a three-dimensional point cloud image.

[0017] FIG2A is a schematic diagram of six viewing angles of a point cloud image.

[0018] FIG2B is a schematic diagram of a data storage format corresponding to a point cloud image.

[0019] FIG3 is a schematic diagram of a network architecture for point cloud encoding and decoding.

[0020] FIG4A is a schematic diagram of a composition framework of a G-PCC encoder.

[0021] FIG4B is a schematic diagram of a composition framework of a G-PCC decoder.

[0022] FIG5A is a schematic diagram of a low plane position in the Z-axis direction.

[0023] FIG5B is a schematic diagram of a high plane position in the Z-axis direction.

[0024] FIG6 is a schematic diagram of a node encoding sequence.

[0025] FIG. 7A is a schematic diagram of plane identification information.

[0026] FIG. 7B is a schematic diagram of another type of planar identification information.

[0027] FIG8 is a schematic diagram of sibling nodes of a current node.

[0028] FIG9 is a schematic diagram of the intersection of a laser radar and a node.

[0029] FIG10 is a schematic diagram of neighborhood nodes at the same partition depth and the same coordinates.

[0030] FIG11 is a schematic diagram showing a current node being located at a low plane position of a parent node.

[0031] FIG12 is a schematic diagram showing a current node being located at a high plane position of a parent node.

[0032] FIG13 is a schematic diagram of predictive coding of planar position information of a laser radar point cloud.

[0033] FIG14 is a schematic diagram of IDCM encoding.

[0034] FIG15 is a schematic diagram of coordinate transformation for obtaining a point cloud using a rotating laser radar.

[0035] FIG16 is a schematic diagram of predictive coding in the X-axis or Y-axis direction.

[0036] FIG. 17A is a schematic diagram showing an angle of the X-plane predicted by the horizontal azimuth angle.

[0037] FIG17B is a schematic diagram showing an angle of the Y plane predicted by the horizontal azimuth angle.

[0038] FIG18 is another schematic diagram of predictive coding in the X-axis or Y-axis direction.

[0039] FIG. 19A is a schematic diagram showing three intersection points included in a sub-block.

[0040] FIG19B is a schematic diagram of a triangular facet set fitted using three intersection points.

[0041] FIG19C is a schematic diagram of upsampling of a triangle face set.

[0042] FIG20 is an example diagram of the RAHT transformation process.

[0043] FIG21 is another example diagram of the RAHT transformation process.

[0044] FIG22 is a schematic diagram of RAHT transformation and inverse RAHT transformation.

[0045] FIG23 is a schematic diagram of the encoding block structure of attribute information.

[0046] FIG24 is a schematic diagram of the overall process of RAHT intra-frame prediction combined with transform coding of attribute information.

[0047] FIG25 is an example diagram of a linear fitting method for the neighborhood attribute information of the current block.

[0048] Figure 26 is a flowchart of the decoding method provided in an embodiment of the present application.

[0049] FIG27 is a schematic structural diagram of a RAHT layer.

[0050] Figure 28 is a flow chart of the encoding method provided in an embodiment of the present application.

[0051] FIG29 is a schematic diagram of the structure of a decoder provided in an embodiment of the present application.

[0052] FIG30 is a schematic diagram of the structure of a decoder provided in another embodiment of the present application.

[0053] Figure 31 is a schematic diagram of the structure of the encoder provided in one embodiment of the present application.

[0054] Figure 32 is a schematic structural diagram of an encoder provided in another embodiment of the present application. DETAILED DESCRIPTION

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] In a two-dimensional image, each pixel contains information and is distributed regularly, so there's no need to record its location. 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 the point cloud. Similar to a two-dimensional image, each location in the acquisition process has corresponding attribute information, typically an RGB color value, which reflects the object's color. For a point cloud, 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 both point location information and point attribute information. Point location information can also be referred to as point geometric information. For example, point geometric information can be the point's three-dimensional coordinates (x, y, z). Point attribute information can include color information and / or reflectance. For example, reflectance can be one-dimensional reflectance information (r). Color information can be information in any color space, or it can be three-dimensional color information, such as RGB. Here, R represents red (red), G represents green (green), and B represents blue (blue). For another example, the color information may be luminance and chrominance (YCbCr, YUV) information, where Y represents brightness (luma), Cb (U) represents blue color difference, and Cr (V) represents red color difference.

[0062] 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.

[0063] 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).

[0064] Point clouds can be divided into the following categories according to the acquisition method:

[0065] Static point cloud: the object is stationary and the device that acquires the point cloud is also stationary;

[0066] Dynamic point cloud: The object is moving, but the device that obtains the point cloud is stationary;

[0067] Dynamic point cloud acquisition: The device used to acquire the point cloud is in motion.

[0068] For example, point clouds can be divided into two categories according to their usage:

[0069] 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;

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] The following describes the related technologies using the G-PCC codec framework and the AVS codec framework as examples.

[0079] 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.

[0080] Figure 4A shows a schematic diagram of the G-PCC encoder's 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 constructed as a prediction tree. During this process, arithmetic coding is performed on the points within the leaf nodes of the partition to generate a binary geometry bitstream. Alternatively, arithmetic coding is performed on the intersections (vertex) generated by the partition (surface fitting is performed based on the intersections) 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 coding is mainly performed on color information. In the process of color information coding, 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 direct RAHT. Both methods convert 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 then arithmetic coding is performed on the quantized coefficients to generate a binary attribute bit stream.

[0081] 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.

[0082] 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).

[0083] For octree geometry encoding (OctGeomEnc), octree 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 2 M . At the same time, K and M must meet the following 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.

[0084] Exemplarily, Fig. 5A and Fig. 5B provide a kind of plane position schematic diagram.Wherein, Fig. 5A shows a kind of low plane position schematic diagram of Z axis direction, and Fig. 5B shows a kind of high plane position schematic diagram of Z axis direction.As shown in Fig. 5A, here (a), (a0), (a1), (a2), (a3) ​​all belong to the low plane position of Z axis direction, take (a) as an example, it can be seen that the four subnodes occupied in the current node are all located at the low plane position of the current node in the Z axis direction, then 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 Fig. 5B, here (b), (b0), (b1), (b2), (b3) all belong to the high plane position of Z axis direction, take (b) as an example, it can be seen that the four subnodes occupied in the current node are located at the high plane position of the current node in the Z axis direction, then it can be considered that the current node belongs to a Z plane and is a high plane in the Z axis direction.

[0085] 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: 10101010. 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.

[0086] 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.

[0087] 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, predictive coding needs to be performed on the planar identifier and planar position information of the node.

[0088] There are currently three judgment conditions in the G-PCC standard for determining whether a node meets the conditions for planar coding. Each of them will be elaborated in detail below.

[0089] I. Judge according to the planar probability of the node in each dimension.

[0090] (1) Determine the local area density (local_node_density) of the current node;

[0091] (2) Determine the probability Prob(i) of the current node in each dimension.

[0092] When the local area density of the node is less than the threshold Th (for example, Th = 3), compare the planar probability Prob(i) of the current node in the three coordinate dimensions 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) is used to indicate whether planar coding is started in each dimension: Eligible i = Prob(i) >= threshold.

[0093] 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 (1)

[0094] 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 (2)

[0095] Here, the update of Prob(i) is specifically as follows: Prob(i) new = (L × Prob(i) + δ(coded node)) / L + 1 (3)

[0096] Where L = 255; in addition, if the coded node is a plane, δ(coded node) is 1; otherwise, δ(coded node) is 0.

[0097] Here, the update of local_node_density is as follows: local_node_density new =local_node_density+4*numSiblings (4)

[0098] 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 the sibling nodes of the current node. Then, the number of sibling nodes of the current node is 5 (including the current node itself).

[0099] Second, determine whether the current layer nodes meet the plane coding requirements based on the point cloud density of the current layer.

[0100] The point density of the current layer 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 infer direct coding model (IDCM) coding is numPointCountRecon, and because the octree is coded in 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。

[0101] 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.

[0102] 3. Determine whether the current node meets the plane coding requirements based on the acquisition parameters of the lidar point cloud.

[0103] 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 not 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.

[0104] Furthermore, for nodes that meet the plane coding conditions, predictive coding may be performed on the plane identification information and the plane position information.

[0105] First, predictive coding of plane identification information.

[0106] Here, only three context information are used for encoding, that is, the plane identification in each coordinate dimension is separately designed for context.

[0107] Secondly, predictive coding of plane position information.

[0108] 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:

[0109] (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;

[0110] (b) Spatial distance between nodes at the same partition depth and coordinates as the current node and the current node: “near” and “far”;

[0111] (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;

[0112] (d) Coordinate dimension (i=0, 1, 2).

[0113] 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".

[0114] 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 within 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.

[0115] 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.

[0116] 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:

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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:

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 top virtual plane.

[0127] 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:

[0128] 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:

[0129] 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.

[0130] 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:

[0131] (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.

[0132] (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.

[0133] (3) The number of sibling nodes of the current node is greater than 1.

[0134] 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.

[0135] Furthermore, the IDCM encoding process is described in detail below.

[0136] 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:

[0137] (1) 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 is not a duplicate point).

[0138] (2) If the number of points numPonts in the current node is less than or equal to 2, the encoding process is as follows:

[0139] i) First encode whether the numPonts of the current node is greater than 1;

[0140] ii) If the current node has only one point and the geometry coding environment is geometry lossless coding, it is necessary to encode that the second point of the current node is not a duplicate point.

[0141] (3) If the number of points numPonts in the current node is greater than 2, the encoding process is as follows:

[0142] i) First encode the numPonts of the current node to be less than or equal to 1;

[0143] ii) 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, it is necessary to perform exponential Golomb decoding on the remaining number of repeated points.

[0144] 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.

[0145] (1) Point cloud facing the human eye.

[0146] (1) If the current node contains only one point, the geometric information of the point in three dimensions will be directly encoded (bypass coding);

[0147] (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])

[0148] 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:

[0149] 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)).

[0150] (2) LiDAR point cloud.

[0151] 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])

[0152] 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:

[0153] After encoding the priority-encoded coordinate axis dirextAxis, the geometric coordinates of the current node are encoded.

[0154] 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)).

[0155] It should be noted here that all geometric accuracy information in the directAxis direction will be encoded here.

[0156] 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 (R, φ, i). 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°.

[0157] In this way, after encoding all the precision of the directAxis coordinate direction, the LaserIdx corresponding to the current node 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:

[0158] After calculating the current node's LaserIdx, the pointLaserIdx of the point is predictively encoded using the current node's LaserIdx. After encoding the current node's LaserIdx, the three-dimensional geometric information of the current node is predictively encoded using the LiDAR acquisition parameters.

[0159] 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 node is first used to obtain the corresponding predicted value of the horizontal azimuth, i.e., φ pred ; Secondly, the node geometry information corresponding to the current node 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:

[0160] φ=arctan(nodePos[1] / nodePos[0])(7)

[0161] 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:

[0162] Furthermore, using the horizontal azimuth angle φ of the node node And the horizontal azimuth angle φ of the previous Laser code point corresponding to the current node pred Calculate the horizontal azimuth prediction value φ corresponding to the current node predPoint , that is, the predicted value of the horizontal azimuth angle as shown in Figures 17A and 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, for the predicted horizontal azimuth angle value φ corresponding to the current node predPoint , calculated as follows:

[0163] 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 current node’s high plane. Indicates the predicted horizontal azimuth angle corresponding to the current node.

[0164] 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.

[0165] After encoding the LaserIdx of the completed point, the Z-axis direction of the current node will be predicted and encoded using the LaserIdx corresponding to the current node. That is, the radius of the radar coordinate system is calculated using the x and y information of the current node. Then, the tangent value of the current node and the vertical offset are obtained using the LaserIdx of the current node. The predicted value of the Z-axis direction of the current node, namely Z_pred, can be obtained. The details are as follows: int tanTheta=tanθ laserIdx ; int zOffset = Z laserIdx ; Z_pred=radius×tanTheta-zOffset.

[0166] Furthermore, Z_pred is used to perform predictive coding on the geometric information of the current node in the Z-axis direction to obtain the prediction residual Z_res, and finally Z_res is encoded.

[0167] 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.

[0168] 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.

[0169] The following is a detailed introduction to the IDCM decoding process.

[0170] 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:

[0171] (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.

[0172] (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.

[0173] (3) The number of sibling nodes of the current node is greater than 1.

[0174] 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.

[0175] Next, decode the number of points numPoints of the current node. The specific decoding method is as follows:

[0176] i) First decode whether numPonts of the current node is greater than 1;

[0177] ii) If the numPonts of the current node is greater than 1, the second point is decoded to determine whether it is a duplicate point. If the second point is not a duplicate point, it can be implicitly inferred that the second type of DCM pattern contains only two points.

[0178] iii) If the numPonts of the current node obtained by decoding is less than or equal to 1, then decode whether the second point is a repeated point; if the second point is not a repeated point, then it can be implicitly inferred that the second type of DCM mode is satisfied, which contains only one point; if the second point obtained by decoding is a repeated point, then it can be inferred that the third type of DCM mode is satisfied, which contains multiple points, but all are repeated points, then decode whether the number of repeated points is greater than 1 (entropy decoding), and if it is greater than 1, decode the number of remaining repeated points (using exponential Columbus decoding).

[0179] 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).

[0180] 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.

[0181] (1) Point cloud facing the human eye.

[0182] (1) If the current node contains only one point, the geometric information of the point in three dimensions will be directly decoded;

[0183] (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]) (10)

[0184] 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:

[0185] After decoding the prioritized axis dirextAxis, the geometric coordinates of the current node are directly decoded. Assuming that 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:

[0186] (2) LiDAR point cloud.

[0187] If the current node contains two points, the priority decoding 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:

[0188] dirextAxis=!(nodePos[0] <nodePos[1])(11)

[0189] 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:

[0190] After decoding the priority coordinate axis dirextAxis, the geometric coordinates of the current node are decoded.

[0191] 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:

[0192] It should be noted here that all geometric accuracy information in the directAxis direction will be decoded here.

[0193] After decoding all the precision of the directAxis coordinate direction, the current node's LaserIdx (nodeLaserIdx) is calculated first. Then, the node's LaserIdx (nodeLaserIdx) is used to predict and decode the point's LaserIdx (pointLaserIdx). The calculation method for the node or point's LaserIdx is the same as that of the encoder. Finally, the current node's LaserIdx and the node's LaserIdx prediction residual information are decoded to obtain ResLaserIdx. The decoding method is as follows:

[0194] PointLaserIdx=nodeLaserIdx+ResLaserIdx(12)

[0195] After decoding the LaserIdx of the current node, the three-dimensional geometric information of the current node is predicted and decoded using the acquisition parameters of the laser radar. The specific algorithm is as follows:

[0196] As shown in Figure 11, first use the LaserIdx corresponding to the current node to obtain the corresponding horizontal azimuth prediction value, that is, Secondly, the node geometry information corresponding to the current node is used to obtain the horizontal azimuth angle corresponding to the node Assuming that the geometric coordinates of the node are nodePos, the horizontal azimuth angle The calculation method between the node geometry information is as follows:

[0197] 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:

[0198] Furthermore, using the horizontal azimuth angle φ of the node node And the horizontal azimuth angle φ of the previous Laser code point corresponding to the current node pred Calculate the horizontal azimuth prediction value φ corresponding to the current node predPoint , which is the predicted value of the horizontal azimuth angle as shown in Figures 17A and 17B. The calculation method is as follows:

[0199] 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.

[0200] After decoding the LaserIdx of the node, the Z-axis direction of the current node is predicted and decoded using the LaserIdx corresponding to the current node. That is, the radius of the radar coordinate system is calculated using the x and y information of the current node. Then, the tangent value of the current node and the vertical offset are obtained using the LaserIdx of the current node. The predicted value of the Z-axis direction of the current node, namely Z_pred, can be obtained. The details are as follows: int tanTheta=tanθ laserIdx ; int zOffset = Z laserIdx ; Z_pred=radius×tanTheta-zOffset.

[0201] Furthermore, the decoded Z_res and Z_pred are used to reconstruct and restore the geometric information of the current node in the Z-axis direction.

[0202] 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.

[0203] 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.

[0204] Predictive geometry coding (PredGeomTree) involves first sorting the input point cloud. Currently used sorting methods 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 assigned to a different laser, and a prediction tree structure is established based on the different lasers. Next, based on the prediction tree structure, 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 a prediction residual. The geometric prediction residual is then quantized using a 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 bitstream.

[0205] 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.

[0206] 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.

[0207] 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:

[0208] Among them, x l ,y l ,z l ∈{0,1} are the binary values ​​corresponding to the highest bit (l=1) to the lowest bit (l=d) of x, y, and z respectively. The Morton code M is to cross-arrange x, y, and z starting from the highest bit. l ,y l ,z l To the lowest bit, the calculation formula of M is as follows:

[0209] Among them, m l′ ∈{0,1} are the values ​​of the highest bit (l′=1) to the lowest bit (l′=3d) of M. 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.

[0210] It can also be understood that for the G-PCC codec framework, the general test conditions are as follows:

[0211] (1) There are 4 test conditions:

[0212] Condition 1: The geometric position is limited and the attributes are lost;

[0213] Condition 2: Geometric position lossless, attribute lossy;

[0214] Condition 3: Geometric position lossless, attribute loss limited;

[0215] Condition 4: Geometric position and attributes are lossless.

[0216] (2) The general test sequence includes four categories: Cat1A, Cat1B, Cat3-fused, and Cat3-frame. Among them, the Cat3-frame point cloud only contains reflectance attribute information, the Cat1A and Cat1B point clouds only contain color attribute information, and the Cat3-fused point cloud contains both color and reflectance attribute information.

[0217] (3) Technical routes: There are two types in total, which are distinguished by the algorithm used for geometric compression.

[0218] Technical route 1: Octree encoding branch.

[0219] 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.

[0220] 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.

[0221] Technical route 2: prediction tree encoding branch.

[0222] 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.

[0223] At the decoding end, the decoding end continuously parses the bitstream to reconstruct the prediction tree structure. 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.

[0224] As mentioned earlier, there are two main transformation methods for encoding attribute information: lifting transformation and RAHT transformation. Lifting transformation predicts and transforms point clouds based on the order in which LODs are generated, while RAHT transformation adaptively transforms attribute information from the bottom up based on the octree's hierarchy. The following section describes RAHT transformation in detail.

[0225] The RAHT transform uses the Haar wavelet transform, which can transform the attribute information of the point cloud from the spatial domain to the frequency domain, thereby further reducing the correlation between the attribute information of the point cloud. Figure 20 is an example diagram of the RAHT transform process. As shown in Figure 20, RAHT performs wavelet transform based on the hierarchical structure of the octree, thereby associating the attribute information with the octree nodes. The attribute information of the occupied nodes in the same parent node is recursively transformed in a bottom-up manner, and the nodes in each layer are transformed from the three dimensions of x, y, and z (see Figure 21) until they are transformed to the root node of the octree. In the process of hierarchical transformation, the direct current (DC) coefficients (or low-pass coefficients) obtained after the transformation of the nodes in the same layer are passed to the nodes in the upper layer for further transformation, and all alternating current (AC) coefficients (or high-pass coefficients) will be quantized and encoded.

[0226] Figure 22 is a schematic diagram of RAHT transformation and inverse RAHT transformation. Assume 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 RAHT 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 . 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 We will continue to search for neighboring points to transform. If no neighboring points are found, we can transform g′ L-1,x,y,z Directly passed to the L-2 layer. That is to say, RAHT transformation is only valid for nodes with neighboring points, and nodes without neighboring points will be directly passed to the previous layer. In the above transformation process, g′ L,2x,y,z The weights corresponding to g′L, 2x+2, y, and z (the weights can be determined based on the number of non-empty child nodes in the node) are w′ 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 of RAHT transformation is:

[0227] In formula (18), T w0,w1 is the transformation matrix, which can be determined based on formula (19):

[0228] The transformation matrix will be updated adaptively as the weights corresponding to each point are transformed. The above transformation process will be iterated and updated continuously according to the partitioning structure of the octree until the root node of the octree is reached.

[0229] Based on the RAHT transform, RAHT intra-frame prediction can be performed on the attribute information, that is, RAHT intra-frame prediction combined with transform coding can be performed on the attribute information. This coding mode is described in detail below.

[0230] As shown in Figure 20, the RAHT transform is based on the order of the octree hierarchy, continuously transforming from the voxel level until the root node is obtained, thereby completing the hierarchical transform coding of the entire attribute information. In RAHT intra-frame prediction combined with transform coding, the attribute information can also be predicted and transformed based on the hierarchical order of the octree. The difference is that the RAHT intra-frame prediction combined with transform coding process can continuously transform from the root node to the voxel level. In each RAHT transform process, the attribute information can be predicted and transformed based on a 2x2x2 block.

[0231] The structure of the encoding block of attribute information can be seen in Figure 23. The dark gray block in Figure 23 is the current block to be encoded, and the light gray block is the neighboring block that is coplanar and colinear with the current block. The attribute information of the current block can be normalized based on equations (20) to (22): node =∑ p∈node attribute(p) (20) w node =∑ p∈node 1=#{p∈node} (21) a node =A node / w node (twenty two)

[0232] Specifically, we can first obtain the attribute information of the current block based on the attribute information of the nodes in the current block, that is, A node For example, a simple sum operation can be performed on the attribute information of the nodes in the current block to determine A node Then, we can use the attribute information of the current block and the number of nodes in the current block (i.e., w node ) is normalized to obtain the mean value a of the attribute information of the current block node Then, the mean value of the attribute information of the current block can be used for transform coding.

[0233] Figure 24 illustrates the overall process of combining RAHT intra-frame prediction with transform coding for attribute information. Figure 24 (d) shows the attribute information of the current block, and Figure 24 (e) shows the attribute information of the predicted block obtained by linear weighted fitting using the neighborhood attribute information of the current block. Attribute transforms can then be performed on the attribute information of the current block and the predicted block, respectively, to obtain DC coefficients and AC coefficients. The AC coefficients can then be predictively coded.

[0234] FIG25 is an example diagram of a linear fitting method for the neighborhood attribute information of the current block. As shown in FIG25 , first, the 19 neighborhood blocks of the current block can be determined. Secondly, the spatial geometric distance between the neighborhood block and each sub-block in the current block can be used to perform linear weighted prediction on the attribute information of each sub-block to obtain the attribute information of the predicted block. Then, the attribute information of the predicted block can be transformed. Exemplarily, equations (23) to (25) can be used to predict and transform the attribute information (Equation (23) represents the transformation method of the attribute information of the current block, equation (24) represents the transformation method of the attribute information of the predicted block, and equation (25) outputs the predicted residual information):

[0235] The above section describes RAHT intra-frame prediction combined with transform coding in detail. In some implementations, RAHT inter-frame prediction combined with transform coding can also be enabled and used in combination with RAHT intra-frame prediction combined with transform coding. This coding mode is described in detail below.

[0236] If inter-frame prediction coding is started, the RAHT attribute transform coding structure will first be constructed based on the geometric information of the current node, that is, the nodes will be continuously merged at the voxel level until the root node of the entire RAHT transform tree is obtained, thereby obtaining the transform coding hierarchical structure corresponding to the attribute information. Then, according to the RAHT transform structure, the root node can be divided to obtain N child nodes of each node (N is less than or equal to 8). Unlike the RAHT intra-frame prediction combined with transform coding mode, the RAHT inter-frame prediction combined with transform coding mode will utilize the node information of the reference frame. For example, the attribute information of the N child nodes of the current node can be RAHT transformed to obtain DC and AC coefficients. Secondly, the AC coefficients of the N child nodes can be inter-frame predicted in the following way.

[0237] For example, if the inter-frame prediction node of the current node is valid (ie, the co-located node of the current node in the reference frame exists), the attribute information of the prediction node is directly used as the attribute prediction value of the current node.

[0238] For another example, if the current node can find a node with exactly the same position as the current node in the cache of the reference frame (that is, the current node exists in the same node in the reference frame), then the attribute prediction values ​​of the AC coefficients of the N child nodes of the current node can be determined based on the AC coefficients of the M child nodes contained in the same node. For example, if the AC coefficient of the inter-frame prediction node corresponding to a child node is not zero, the AC coefficient of the inter-frame prediction node is directly used as the prediction value of the child node; if the AC coefficient of the inter-frame prediction node corresponding to a child node is zero, the AC coefficient of the intra-frame prediction node corresponding to the child node can be used as the prediction value.

[0239] For another example, if the inter-frame prediction node of the current node is invalid (ie, the co-located node of the current node in the reference frame does not exist), the attribute prediction value of the adjacent node in the frame can be used as the attribute prediction value of the current node.

[0240] In addition, after RAHT inter prediction is enabled, the optimal RAHT prediction mode can be selected for each layer. The RAHT prediction mode can be either RAHT intra prediction mode or RAHT inter prediction mode. If the cost of the RAHT intra prediction mode is less than the cost of the RAHT inter prediction mode, RAHT intra prediction can be performed on the current layer; otherwise, RAHT inter prediction is performed.

[0241] In related art, if RAHT inter-frame prediction is enabled, the optimal RAHT prediction mode is selected between RAHT inter-frame prediction and RAHT intra-frame prediction. However, in some cases, the prediction performance of both RAHT inter-frame prediction and RAHT intra-frame prediction is relatively poor. In such cases, adopting the solution provided by related art will result in poor encoding and decoding performance of attribute information.

[0242] In response to the above problems, an embodiment of the present application provides a coding method, including: determining a target coding mode corresponding to a current layer from a plurality of coding modes, the plurality of coding modes including RAHT transform coding and RAHT prediction combined with transform coding; encoding attribute information of the nodes of the current layer according to the target coding mode.

[0243] An embodiment of the present application also provides a decoding method, including: parsing a bitstream, determining first information, where the first information is used to indicate a target decoding mode from a plurality of decoding modes corresponding to a current layer, the plurality of decoding modes including RAHT transform decoding, and RAHT prediction combined with transform decoding; decoding attribute information of a node of the current layer according to the target decoding mode.

[0244] In the related art, after starting RAHT prediction combined with transform coding, RAHT prediction needs to be performed on the basis of RAHT transform. The RAHT prediction process is relatively complicated. If the RAHT prediction performance is poor, forcing RAHT prediction will not significantly improve the coding efficiency of point cloud attribute information. At this time, the existence of the RAHT prediction process will lead to an increase in coding complexity and introduce more additional coding losses. It is precisely in view of the above problems that the embodiment of the present application proposes to select a coding mode between RAHT prediction combined with transform coding and RAHT transform coding. That is to say, in the embodiment of the present application, even if RAHT prediction combined with transform coding is started, it is possible to revert to RAHT transform coding. Therefore, when the RAHT prediction performance is poor, a scheme with lower coding complexity and smaller coding loss can be selected, thereby optimizing the coding performance.

[0245] The decoding method provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0246] Figure 26 is a flow chart of the decoding method provided in an embodiment of the present application. The decoding method of Figure 26 can be applied to a decoder. The decoding method of Figure 26 can be used to decode the attribute information of a point cloud. In some implementations, the decoding method can be applied to G-PCC. Alternatively, in other implementations, the decoding method can be applied to a geometry explore solid test model (GES-TM). GES-TM is a coding and decoding framework proposed for dense point clouds (such as point clouds collected in augmented reality (AR) or virtual reality (VR) scenes).

[0247] Referring to FIG. 26 , in step S2610 , the code stream is parsed to determine the first information.

[0248] The first information is used to indicate a target decoding mode from a plurality of decoding modes corresponding to the current layer.

[0249] The first information may be carried at any position in the code stream. For example, the first information may be carried in an attribute brick header (ABH).

[0250] The first information may correspond to a RAHT layer, that is, the first information may be information at a RAHT level.

[0251] The current layer may also be referred to as a RAHT layer, a RAHT decoding layer, a RAHT attribute decoding layer, or a RAHT transform layer. The current layer may be determined based on at least one division (or downsampling) of the previous layer of the current layer along the X, Y, and Z directions. For example, the RAHT transform decoding order is usually performed from the root node to the voxel level (1x1x1), thereby completing the decoding and attribute reconstruction of the attribute information of the entire point cloud. Therefore, referring to FIG27 , it can be defined that each layer obtained by dividing or downsampling along the X, Y, and Z directions is a RAHT layer.

[0252] The target decoding mode indicated by the first information can be referred to as the optimal decoding mode for the current layer. The target decoding mode can be determined based on the rate-distortion costs of multiple decoding modes. For example, the target decoding mode can be the decoding mode with the lowest rate-distortion cost among the multiple decoding modes. The calculation method of the rate-distortion cost can be found in the description of the encoding side and is not described in detail here.

[0253] The above-mentioned multiple decoding modes may include RAHT transform decoding and RAHT prediction combined with transform decoding. In other words, the embodiments of the present application will consider the decoding performance of RAHT prediction combined with transform decoding and RAHT transform decoding at the same time. In the case of poor RAHT prediction performance, the introduction of RAHT transform decoding helps to improve the decoding performance of attribute information.

[0254] The embodiments of the present application do not specifically limit the decoding method of RAHT prediction combined with transform decoding. For example, RAHT prediction combined with transform decoding may include at least one of the following: RAHT intra-frame prediction combined with transform decoding, RAHT inter-frame prediction combined with transform decoding, or a combination of RAHT intra-frame prediction combined with transform decoding and RAHT inter-frame prediction combined with transform decoding (for example, if the node to be decoded has a co-located node in the reference frame, RAHT inter-frame prediction combined with transform is performed; if there is no co-located node, RAHT intra-frame prediction combined with transform decoding is performed).

[0255] In some implementations, the RAHT prediction combined with transform decoding includes RAHT inter-frame prediction combined with transform decoding, and a reference frame for the RAHT inter-frame prediction combined with transform decoding is a reference frame that has undergone global motion compensation or local motion compensation.

[0256] In some implementations, the multiple decoding modes mentioned above may include RAHT transform decoding, RAHT intra prediction combined with transform decoding, a combination of RAHT intra prediction combined with transform decoding, and RAHT inter prediction combined with transform decoding. That is, the optimal decoding mode can be selected from the combination of RAHT transform decoding, RAHT intra prediction combined with transform decoding, RAHT intra prediction combined with transform decoding, and RAHT inter prediction combined with transform decoding. If the decoding performance of RAHT prediction combined with transform decoding is poor, further consideration of RAHT transform decoding (i.e., no prediction, only RAHT transform) may help improve the decoding performance of attribute information.

[0257] In some implementations, the multiple decoding modes mentioned above may include RAHT transform decoding, RAHT intra prediction combined with transform decoding, and RAHT inter prediction combined with transform decoding. That is, an optimal decoding mode may be selected from RAHT transform decoding, RAHT intra prediction combined with transform decoding, and RAHT inter prediction combined with transform decoding. If the decoding performance of RAHT prediction combined with transform decoding is poor, further consideration of RAHT transform decoding (i.e., performing only RAHT transform without prediction) may help improve the decoding performance of attribute information.

[0258] In some implementations, whether to execute step S2610 may be determined based on certain conditions. For example, it may first be determined whether the attribute information of the nodes in the current layer meets the conditions for performing inter-frame prediction. If the attribute information meets the conditions for performing inter-frame prediction, step S2610 is executed; otherwise, step S2610 may not be executed. The inter-frame prediction condition may, for example, be whether the current frame is a P frame or a B frame.

[0259] In some implementations, before determining whether the attribute information of a node in the current layer satisfies the conditions for inter-frame prediction, it may be determined whether the current layer can employ attribute prediction decoding. For example, whether the current layer can employ attribute prediction decoding may be determined based on the number of neighboring nodes in the current layer and / or the number of neighboring nodes of the parent node of the node in the current layer. Exemplarily, if the number of neighboring nodes in the current layer is greater than or equal to a first threshold and / or the number of neighboring nodes of the parent node of the node in the current layer is greater than or equal to a second threshold, it is determined that the current layer can employ attribute prediction decoding.

[0260] In some implementations, if the attribute information of the node in the current layer does not meet the conditions for inter-frame prediction, RAHT transform decoding and / or RAHT intra-frame prediction combined with transform decoding are performed on the attribute information.

[0261] Continuing with Figure 26, in step S2620, attribute information of the nodes in the current layer is decoded according to the target decoding mode. For example, the bitstream may be parsed to determine the quantization coefficients of the nodes in the current layer; then, the quantization coefficients may be dequantized to obtain the AC coefficients of the nodes in the current layer; then, the AC coefficients may be predicted and / or inversely transformed according to the target decoding mode to determine the reconstructed attribute information of the nodes in the current layer.

[0262] In some implementations, if the target decoding mode is a combination of RAHT intra-frame prediction combined with transform decoding and RAHT inter-frame prediction combined with transform decoding, step S2620 may include: determining whether the inter-frame prediction value of the AC coefficient of the node in the current layer is equal to zero; if the inter-frame prediction value is not equal to zero, the prediction value of the AC coefficient may be determined as the inter-frame prediction value; if the inter-frame prediction value is equal to zero, the prediction value of the AC coefficient of the node in the current layer may be determined as the intra-frame prediction value.

[0263] In some implementations, if the target decoding mode is a combination of RAHT intra prediction combined with transform decoding and RAHT inter prediction combined with transform decoding, step S2620 may include: determining an intra prediction value and an inter prediction value for an AC coefficient of a node in the current layer according to the target decoding mode; fusing the intra prediction value and the inter prediction value to determine a target prediction value for the AC coefficient; and determining reconstructed attribute information for the node in the current layer based on the target prediction value. Fusing the intra prediction result and the inter prediction result helps improve the accuracy of the prediction information, thereby improving the decoding performance of the attribute information.

[0264] For example, the arithmetic mean of the intra-frame prediction value and the inter-frame prediction value can be used as the target prediction value.

[0265] For another example, the intra-frame prediction value and the inter-frame prediction value can be weighted and summed to determine the target prediction value. Assuming that the intra-frame prediction value of the AC coefficient of the current node (which can be any node in the current layer) is predIntraVal and the inter-frame prediction value is predInterVal, the target prediction value predVal can be calculated using formula (26): predVal = w1*predIntraVal + w2*predIntraVal (26)

[0266] Where w1 and w2 represent the weights of predIntraVal and predIntraVal respectively.

[0267] The weights of the intra-frame prediction value and the inter-frame prediction value of the AC coefficient of the current node can be determined in a variety of ways, which are not specifically limited in the embodiments of the present application. For example, the weights of the intra-frame prediction value and the inter-frame prediction value can be determined based on the number of neighboring nodes during intra-frame prediction. The greater the number of neighboring nodes during intra-frame prediction, the higher the weight of the intra-frame prediction value. Alternatively, multiple weights can be predefined for the intra-frame prediction value and the inter-frame prediction value, and then the optimal weight can be determined based on a rate-distortion optimization algorithm.

[0268] As mentioned above, in some implementations, multiple decoding modes may include RAHT inter-frame prediction combined with transform decoding. The reference frame for this RAHT inter-frame prediction combined with transform decoding can be either a non-motion-compensated reference frame or a motion-compensated reference frame. Furthermore, in some implementations, a first reference frame and a second reference frame can be simultaneously introduced for RAHT inter-frame prediction combined with transform decoding. The first reference frame is a reference frame that has undergone motion compensation (which can be local motion compensation or global motion compensation), and the second reference frame is a reference frame that has not undergone motion compensation (which can be local motion compensation or global motion compensation). At the encoder, inter-frame prediction can be performed based on the first and second reference frames, and an appropriate reference frame can be selected based on the rate-distortion cost. Second information can then be written into the bitstream to indicate the reference frame corresponding to the RAHT inter-frame prediction combined with transform decoding. At the decoder, the bitstream can be parsed to determine the reference frame corresponding to the RAHT inter-frame prediction combined with transform decoding. Considering both motion-compensated and non-motion-compensated reference frames helps improve encoding and decoding performance.

[0269] In some implementations, the code stream may be parsed to determine the third information. The third information is used to indicate whether a motion-compensated reference frame will be used for RAHT inter-frame prediction. For example, the third information may include a first value (e.g., 1 or true) and a second value (e.g., 0 or false). If the value of the third information is the first value, it indicates that the motion-compensated reference frame can be used for RAHT inter-frame prediction; if the value of the third information is the second value, it indicates that only non-motion-compensated reference frames are used for RAHT inter-frame prediction.

[0270] The third information may correspond to a slice (also referred to as a stripe). That is, the third information may be slice-level information, used to indicate whether a slice can use a motion-compensated reference frame for RAHT inter-frame prediction.

[0271] Alternatively, the third information may correspond to a RAHT layer. That is, the third information may be RAHT layer-level information, used to indicate whether a RAHT layer can use a reference frame after motion compensation for RAHT inter-frame prediction.

[0272] The multiple coding modes mentioned above are set to the following three coding modes: RAHT transform decoding, RAHT intra-frame prediction combined with transform decoding, and a combination of RAHT intra-frame prediction combined with transform decoding and RAHT inter-frame prediction combined with transform decoding. Tables 1 and 2 show the test results of the solution provided in the embodiment of the present application (for comparison, the RAHT inter-frame prediction solution provided in the related art is used):

[0273] Table 1: Coding performance test results under geometric lossless and attribute lossy conditions

[0274] Table 2: Coding performance test results under geometric lossy and attribute lossy conditions

[0275] In Tables 1 and 2, Luma represents luminance, and Cb and Cr represent chrominance. The test results in Tables 1 and 2 show that, under test condition C1, the scheme provided by the present embodiment achieves a 6.7% performance gain on the video sequence 8ivfbv2_soldier_vox10 compared to the RAHT inter-frame prediction scheme provided by the related art. Under test condition C2, the scheme provided by the present embodiment achieves a 12.3% performance gain on the video sequence 8ivfbv2_soldier_vox10 compared to the RAHT inter-frame prediction scheme provided by the related art. This performance gain is achieved by taking into account that when RAHT prediction performance is poor, a rate-distortion optimization algorithm can be used to revert to the RAHT transform coding mode, thereby improving overall coding performance.

[0276] The decoding method provided by the embodiment of the present application is described in detail above in conjunction with Figure 26. The encoding method provided by the embodiment of the present application is described in detail below in conjunction with Figure 28.

[0277] Figure 28 is a flow chart of the encoding method provided in an embodiment of the present application. The encoding method of Figure 28 can be applied to an encoder. The encoding method of Figure 28 can be used to encode the attribute information of a point cloud. In some implementations, the encoding method can be applied to G-PCC. Alternatively, in other implementations, the encoding method can be applied to GES-TM. GES-TM is a coding framework proposed for dense point clouds (such as point clouds collected in AR or VR scenes).

[0278] 28 , in step S2810 , a target coding mode corresponding to the current layer is determined from a plurality of coding modes.

[0279] The current layer may also be referred to as a RAHT layer, a RAHT coding layer, a RAHT attribute coding layer, or a RAHT transform layer. The current layer may be determined based on at least one division (or downsampling) of the previous layer of the current layer along the X, Y, and Z directions. For example, the RAHT transform coding order is usually divided sequentially from the root node to the voxel level (1x1x1), thereby completing the encoding and attribute reconstruction of the entire point cloud attribute information. Therefore, referring to FIG. 27 , it may be defined that each layer obtained by dividing or downsampling along the X, Y, and Z directions is a RAHT layer.

[0280] The target coding mode may be referred to as the optimal coding mode for the current layer. The target coding mode may be determined based on the rate-distortion cost of multiple coding modes. For example, the target coding mode may be the coding mode with the lowest rate-distortion cost among the multiple coding modes.

[0281] The rate-distortion cost of multiple coding modes can be determined based on the distortion parameters, bit rate parameters, and Lagrange factors corresponding to the multiple coding modes. For example, for each coding mode, the distortion parameter can be determined based on the reconstructed attribute information and the original attribute information of each coding mode; then, the number of coding bits required for each coding mode can be determined, thereby determining the bit rate parameter. Then, the rate-distortion cost of each coding mode can be determined based on the following formula (27):

[0282] J=D+λxR(27)

[0283] Where J represents the rate-distortion cost, D represents the distortion parameter, R represents the bit rate parameter, and λ represents the Lagrangian factor.

[0284] The Lagrangian factor can be determined based on the quantization parameter of the attribute information of the current layer node. For example, the value of λ can be determined based on the following formula (28):

[0285] Where QP represents the quantization parameter of the attribute information. N can be a predefined value. The value of N can be associated with the type of attribute information. For example, the value of N can be different for reflectivity and color information.

[0286] The above-mentioned multiple coding modes may include RAHT transform coding and RAHT prediction combined with transform coding. That is, in the embodiment of the present application, when RATH prediction combined with transform coding is enabled, not only RATH prediction combined with transform coding is considered, but RATH prediction combined with transform coding and RAHT transform coding are considered simultaneously, which helps to improve the encoding performance of attribute information.

[0287] The embodiments of the present application do not specifically limit the coding method of RAHT prediction combined with transform coding. For example, RAHT prediction combined with transform coding may include at least one of the following: RAHT intra-frame prediction combined with transform coding, RAHT inter-frame prediction combined with transform coding, or a combination of RAHT intra-frame prediction combined with transform coding and RAHT inter-frame prediction combined with transform coding (for example, if the node to be coded has a co-located node in the reference frame, RAHT inter-frame prediction combined with transform coding is performed; if there is no co-located node, RAHT intra-frame prediction combined with transform coding is performed).

[0288] In some implementations, the RAHT prediction combined with transform coding includes RAHT inter-frame prediction combined with transform coding, and the reference frame of the RAHT inter-frame prediction combined with transform coding is a reference frame that has undergone global motion compensation or local motion compensation.

[0289] In some implementations, the multiple coding modes mentioned above may include RAHT transform coding, RAHT intra-frame prediction combined with transform coding, a combination of RAHT intra-frame prediction combined with transform coding, and RAHT inter-frame prediction combined with transform coding. That is, an optimal coding mode may be selected from the combination of RAHT transform coding, RAHT intra-frame prediction combined with transform coding, RAHT intra-frame prediction combined with transform coding, and RAHT inter-frame prediction combined with transform coding. If the coding performance of RAHT prediction combined with transform coding is poor, further consideration of RAHT transform coding (i.e., performing only RAHT transform without prediction) may help improve the coding performance of attribute information.

[0290] In some implementations, the multiple coding modes mentioned above may include RAHT transform coding, RAHT intra-frame prediction combined with transform coding, and RAHT inter-frame prediction combined with transform coding. That is, an optimal coding mode may be selected from RAHT transform coding, RAHT intra-frame prediction combined with transform coding, and RAHT inter-frame prediction combined with transform coding. If the coding performance of RAHT prediction combined with transform coding is poor, further consideration of RAHT transform coding (i.e., performing only RAHT transform without prediction) may help improve the coding performance of attribute information.

[0291] In some implementations, whether to execute step S2810 may be determined based on certain conditions. For example, it may be first determined whether the attribute information of the node in the current layer satisfies the conditions for inter-frame prediction. If the attribute information satisfies the conditions for inter-frame prediction, step S2810 is executed; otherwise, step S2810 may not be executed. The conditions for inter-frame prediction may be, for example, whether the current frame is a P frame or a B frame. That is, when performing RAHT inter-frame prediction on attribute information, if the current layer can perform RAHT attribute inter-frame prediction, multiple coding modes may be introduced for the current layer, and then the best coding mode may be selected from the multiple coding modes (such as selection using a rate-distortion optimization algorithm) for encoding, thereby improving the coding efficiency of the point cloud attribute information.

[0292] In some implementations, before determining whether the attribute information of a node in the current layer satisfies the conditions for inter-frame prediction, it may be determined whether the current layer can employ attribute prediction coding. For example, whether the current layer can employ attribute prediction coding may be determined based on the number of neighboring nodes in the current layer and / or the number of neighboring nodes of the parent node of the node in the current layer. Exemplarily, if the number of neighboring nodes in the current layer is greater than or equal to a first threshold and / or the number of neighboring nodes of the parent node of the node in the current layer is greater than or equal to a second threshold, it is determined that the current layer can employ attribute prediction coding.

[0293] In some implementations, if the attribute information of the node in the current layer does not meet the conditions for inter-frame prediction, RAHT transform coding and / or RAHT intra-frame prediction combined with transform coding are performed on the attribute information.

[0294] Continuing with FIG. 28 , in step S2820 , attribute information of the nodes in the current layer is encoded according to the target coding mode. For example, the attribute information of the nodes in the current layer can be encoded according to the target coding mode to determine AC coefficients. Then, quantization coefficients can be determined based on the AC coefficients, and coded bits corresponding to the attribute information can be determined based on the quantization coefficients.

[0295] In some implementations, the method of FIG. 28 may further include writing first information into the bitstream. The first information is used to indicate a target coding mode. In other words, the encoder may transmit the optimal coding mode for the current layer (e.g., the coding mode with the lowest rate-distortion cost) to the decoder via the first information. The decoder may analyze the first information to determine the optimal decoding mode, thereby reconstructing and recovering the attribute information of the nodes in the current layer.

[0296] The first information may be carried at any position in the code stream, for example, in an attribute block header.

[0297] The first information may correspond to a RAHT layer, that is, the first information may be information at a RAHT level.

[0298] In some implementations, if the target coding mode is a combination of RAHT intra-frame prediction combined with transform coding and RAHT inter-frame prediction combined with transform coding, step S2820 may include: determining whether the inter-frame prediction value of the AC coefficient of the node in the current layer is equal to zero; if the inter-frame prediction value is not equal to zero, the prediction value of the AC coefficient may be determined as the inter-frame prediction value; if the inter-frame prediction value is equal to zero, the prediction value of the AC coefficient of the node in the current layer may be determined as the intra-frame prediction value.

[0299] In some implementations, if the target coding mode is a combination of RAHT intra prediction combined with transform coding and RAHT inter prediction combined with transform coding, step S2820 may include: determining intra prediction values ​​and inter prediction values ​​for AC coefficients of nodes in the current layer according to the target coding mode; fusing the intra prediction values ​​and inter prediction values ​​to determine a target prediction value for the AC coefficient; and determining reconstructed attribute information for the nodes in the current layer based on the target prediction value. Fusing the intra prediction results and inter prediction results helps improve the accuracy of the prediction information, thereby improving the encoding performance of the attribute information.

[0300] For example, the arithmetic mean of the intra-frame prediction value and the inter-frame prediction value can be used as the target prediction value.

[0301] For another example, the intra-frame prediction value and the inter-frame prediction value can be weighted and summed to determine the target prediction value. Assuming that the intra-frame prediction value of the AC coefficient of the current node (which can be any node in the current layer) is predIntraVal and the inter-frame prediction value is predInterVal, the target prediction value predVal can be calculated using formula (29): predVal = w1*predIntraVal + w2*predIntraVal (29)

[0302] Where w1 and w2 represent the weights of predIntraVal and predIntraVal respectively.

[0303] The weights of the intra-frame prediction value and the inter-frame prediction value of the AC coefficient of the current node can be determined in a variety of ways, and the embodiments of the present application do not specifically limit this. For example, the weights of the intra-frame prediction value and the inter-frame prediction value can be determined based on the number of neighboring nodes during intra-frame prediction. The more neighboring nodes there are during intra-frame prediction, the higher the weight of the intra-frame prediction value. Alternatively, multiple weights can be predefined for the intra-frame prediction value and the inter-frame prediction value, and then the optimal weight can be determined based on the rate-distortion optimization algorithm. Furthermore, the index information corresponding to the optimal weight can be transmitted to the decoding end.

[0304] As mentioned above, in some implementations, multiple coding modes may include RAHT inter-frame prediction combined with transform coding. The reference frame for this RAHT inter-frame prediction combined with transform coding can be either a non-motion-compensated reference frame or a motion-compensated reference frame. Furthermore, in some implementations, a first reference frame and a second reference frame can be simultaneously introduced for RAHT inter-frame prediction combined with transform coding. The first reference frame is a reference frame that has undergone motion compensation (which can be local motion compensation or global motion compensation), and the second reference frame is a reference frame that has not undergone motion compensation (which can be local motion compensation or global motion compensation). At the encoder, inter-frame prediction can be performed based on the first and second reference frames, and an appropriate reference frame can be selected based on the rate-distortion cost. Second information can then be written into the bitstream to indicate the reference frame corresponding to the RAHT inter-frame prediction combined with transform coding. At the decoder, the bitstream can be parsed to determine the reference frame corresponding to the RAHT inter-frame prediction combined with transform decoding. Considering both motion-compensated and non-motion-compensated reference frames helps improve encoding performance. The above-mentioned RAHT inter-frame prediction combined with transform coding can be a slice-level coding mode, that is, if a slice introduces the above-mentioned RAHT inter-frame prediction combined with transform coding, the attribute information of the slice can all adopt the RAHT inter-frame prediction combined with transform coding.

[0305] In some implementations, third information may be written into the bitstream. The third information is used to indicate whether a motion-compensated reference frame will be used for RAHT inter-frame prediction. For example, the third information may include a first value (e.g., 1 or true) and a second value (e.g., 0 or false). If the value of the third information is the first value, it indicates that the motion-compensated reference frame can be used for RAHT inter-frame prediction; if the value of the third information is the second value, it indicates that only non-motion-compensated reference frames are used for RAHT inter-frame prediction.

[0306] The third information may correspond to a slice (also referred to as a stripe). That is, the third information may be slice-level information, used to indicate whether a slice can use a motion-compensated reference frame for RAHT inter-frame prediction.

[0307] Alternatively, the third information may correspond to a RAHT layer. That is, the third information may be RAHT layer-level information, used to indicate whether a RAHT layer can use a reference frame after motion compensation for RAHT inter-frame prediction.

[0308] The following examples are used to describe the embodiments of the present application in more detail. It should be noted that the examples below are only intended to help those skilled in the art understand the embodiments of the present application, rather than to limit the embodiments of the present application to the specific numerical values ​​or specific scenarios illustrated. It is apparent that those skilled in the art can make various equivalent modifications or changes based on the examples given below, and such modifications or changes also fall within the scope of the embodiments of the present application.

[0309] In the GES-TM attribute RAHT inter-frame prediction coding provided by the related art, the syntax elements in the high-level adaptation parameter set (aps) can be used to determine whether to use inter-frame prediction or intra-frame prediction to predictively encode point cloud attribute information. In addition, a syntax element (treeDepth) can be used to determine the starting layer number for inter-frame prediction. In other words, RAHT layers below this starting layer number only use RAHT intra-frame prediction. The above attribute coding scheme has two major problems. First, this attribute coding scheme does not analyze the distribution of AC coefficients of different RAHT layers in different slices, but instead directly determines the inter-frame coding scheme for the attribute information of the current sequence in the sequence set. Second, the number of inter-frame prediction layers is determined in the aps because the intra-frame correlation of the AC coefficients of the lower layers in the RAHT layer structure is stronger than the inter-frame correlation. However, this coding scheme does not fully and effectively utilize the distribution of AC coefficients of different RAHT layers, resulting in low coding efficiency of the attribute information. Based on the above problems, this example introduces a new coding scheme. First, three attribute information encoding modes are introduced: RAHT intra-frame prediction + transform coding, RAHT inter-frame prediction + RAHT intra-frame prediction + transform coding, and RAHT transform coding. Next, before encoding the AC coefficients of different RAHT layers, the encoder uses a rate-distortion optimization algorithm to determine the optimal coding mode for the current layer. This optimal coding mode for the current layer is then passed to the decoder, which uses it to adaptively restore the AC coefficients of the current layer, thereby completing the encoding and decoding of point cloud attribute information and ultimately improving the coding efficiency of point cloud attributes.

[0310] In the implementation, this example first defines the RAHT layer. The current RAHT transformation order for attribute information is to sequentially partition from the root node down to the voxel level (1x1x1), thereby completing the encoding and reconstruction of the entire point cloud attribute information. Based on this, we can define each layer obtained by downsampling along the Z, Y, and X directions as a RAHT layer. See Figure 27 for details.

[0311] Secondly, based on the RAHT layer, the coding mode of the current layer is adaptively selected from the following three coding modes: RAHT intra-frame prediction + transform coding; RAHT inter-frame prediction + RAHT intra-frame prediction + transform coding; and RAHT transform coding. The rate-distortion optimization algorithm can be used to obtain the optimal coding mode of the current layer from the three coding modes, and the optimal coding mode is passed to the decoding end. The decoding end uses the best decoding mode obtained by parsing to reconstruct and restore the attribute information of the nodes in the current layer. When performing rate-distortion optimization, the distortion parameter D of the reconstructed attribute information and the original attribute information of each coding mode is first calculated, and then the code rate parameter R of each coding mode is obtained. Then, the rate-distortion cost of each coding mode can be calculated based on the following formula: J = D + λxR (30)

[0312] Among them, λ can be calculated through attribute quantization parameters, and the calculation method of λ is as follows:

[0313] The parameter N can be set to different values ​​according to reflectivity and color.

[0314] After obtaining the best coding mode for each layer, the best coding mode for each layer may be added to the attribute block header.

[0315] The specific algorithm on the encoding side is as follows:

[0316] Step 1: Adaptively determine whether the nodes in the current layer can use attribute prediction based on the number of neighboring nodes in the current layer and the number of neighboring nodes of the parent node.

[0317] Step 2: If attribute prediction and attribute inter-frame prediction are available for the nodes in the current layer, a rate-distortion optimization algorithm is introduced to determine the optimal coding mode for the current layer. For example, the optimal coding mode can be determined by encoding each node in the current layer and calculating the cost of each of the three coding modes.

[0318] Step 3: Use the best coding mode to encode the attribute information of the current layer node.

[0319] The specific algorithm of the decoding end is as follows:

[0320] Step 1: Adaptively determine whether the nodes in the current layer can use attribute prediction based on the number of neighboring nodes in the current layer and the number of neighboring nodes of the parent node.

[0321] Step 2: If the nodes in the current layer can use attribute prediction and can perform attribute inter-frame prediction, the optimal decoding mode of the current layer is obtained by parsing the bitstream.

[0322] Step 3: Use the best decoding mode to decode the attribute information of the current layer node.

[0323] In this example, when performing RAHT inter-frame prediction on attribute information, if the current layer can perform attribute inter-frame prediction, three coding modes will be introduced to the current layer first, and then the optimal coding mode will be selected for encoding using the rate-distortion optimization algorithm, thereby improving the coding efficiency of point cloud attributes.

[0324] The syntax elements in the attribute data unit header (Attribute data unit header syntax) are as follows:

[0325] The syntax elements from the third to eighth lines in the above syntax elements are newly introduced in this example based on the syntax elements provided by the related art. According to this syntax element, when the point cloud attribute information adopts RAHT inter-frame prediction (attr_coding_type == 0 && disableAttrInterPred), this example selects the best coding mode from multiple coding modes (the number of coding modes is determined by attr_code_mode_cnt). For other contents of the above syntax elements, please refer to the related art and will not be described in detail here.

[0326] The method embodiment of the present application is described in detail above in conjunction with Figures 1 to 28. The device embodiment of the present application is described in detail below in conjunction with Figures 29 to 32. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment. Therefore, for parts not described in detail, reference can be made to the above method embodiment.

[0327] FIG29 is a schematic diagram of the structure of a decoder provided by an embodiment of the present application. As shown in FIG29 , the decoder 2900 may include a first decoding unit 2910 and a second decoding unit 2920 .

[0328] The first decoding unit 2910 is configured to parse the code stream and determine first information, where the first information is used to indicate a target decoding mode from multiple decoding modes corresponding to the current layer, where the multiple decoding modes include RAHT transform decoding and RAHT prediction combined with transform decoding.

[0329] The second decoding unit 2920 is configured to decode the attribute information of the node of the current layer according to the target decoding mode.

[0330] In some implementations, the RAHT prediction combined with transform decoding includes at least one of: RAHT intra prediction combined with transform decoding; RAHT inter prediction combined with transform decoding; a combination of RAHT intra prediction combined with transform decoding and RAHT inter prediction combined with transform decoding.

[0331] In some implementations, the multiple decoding modes include: RAHT transform decoding; RAHT intra prediction combined with transform decoding; a combination of RAHT intra prediction combined with transform decoding and RAHT inter prediction combined with transform decoding.

[0332] In some implementations, the multiple decoding modes include: RAHT transform decoding; RAHT intra prediction combined with transform decoding; and RAHT inter prediction combined with transform decoding.

[0333] In some implementations, the first decoding unit 2910 is configured to: determine whether the attribute information meets the conditions for inter-frame prediction; if the attribute information meets the conditions, parse the code stream to determine the first information.

[0334] In some implementations, the decoder 2900 further includes: a third decoding unit configured to perform RAHT transform decoding and / or RAHT intra prediction combined with transform decoding on the attribute information if the attribute information does not meet the condition.

[0335] In some implementations, the target decoding mode is a combination of RAHT intra-frame prediction combined with transform decoding and RAHT inter-frame prediction combined with transform decoding, and the second decoding unit 2920 is configured to determine the intra-frame prediction value and the inter-frame prediction value of the AC coefficient of the node in the current layer according to the target decoding mode; fuse the intra-frame prediction value and the inter-frame prediction value to determine the target prediction value of the AC coefficient; and determine the reconstruction attribute information of the node in the current layer according to the target prediction value.

[0336] In some implementations, the multiple decoding modes include RAHT inter-frame prediction combined with transform decoding, and the reference frame of the RAHT inter-frame prediction combined with transform decoding is determined from a first reference frame and a second reference frame, the first reference frame is a reference frame after motion compensation, and the second reference frame is a reference frame that has not undergone motion compensation.

[0337] In some implementations, the motion compensation is local motion compensation or global motion compensation.

[0338] In some implementations, the decoder 2900 further includes: a fourth decoding unit, configured to parse the bitstream and determine second information if the target decoding mode is RAHT inter-frame prediction combined with transform decoding, where the second information is used to indicate a reference frame corresponding to the RAHT inter-frame prediction combined with transform decoding.

[0339] In some implementations, the decoder 2900 further includes: a fourth decoding unit configured to parse the code stream and determine third information, where the third information is used to indicate whether the reference frame after motion compensation will be used for RAHT inter-frame prediction.

[0340] In some implementations, the third information corresponds to a slice or to a RAHT layer.

[0341] In some implementations, the second decoding unit 2920 is configured to: parse the code stream to determine the quantization coefficient of the current layer node; dequantize the quantization coefficient to obtain the AC coefficient of the current layer node; and deconvert the AC coefficient according to the target decoding mode to determine the reconstruction attribute information of the current layer node.

[0342] In some implementations, the current layer is determined based on dividing a previous layer of the current layer at least once along an X direction, a Y direction, and a Z direction.

[0343] In some implementations, the first information is included in an attribute block header.

[0344] In some implementations, the RAHT prediction combined with transform decoding includes RAHT inter-frame prediction combined with transform decoding, and a reference frame for the RAHT inter-frame prediction combined with transform decoding is a reference frame that has undergone global motion compensation or local motion compensation.

[0345] In some implementations, the decoding method is applied to GES-TM.

[0346] It is understandable that in the embodiments of the present application, a "unit" can be a portion of a circuit, a portion of a processor, a portion of a program or software, etc., and of course it can also be a module, or it can be non-modular. Moreover, the various components in this embodiment can be integrated into a processing unit, or each unit 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 in the form of software functional modules.

[0347] If the integrated unit is implemented in the form of a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in this embodiment. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0348] Therefore, an embodiment of the present application provides a computer-readable storage medium, which is applied to the decoder 2900. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the decoding method described in any one of the aforementioned embodiments.

[0349] Based on the composition of the above-mentioned decoder 2900 and the computer-readable storage medium, refer to Figure 30, which shows a specific hardware structure diagram of the encoder 2900 provided in an embodiment of the present application. As shown in Figure 30, the encoder 3000 may include: a communication interface 3010, a memory 3020 and a processor 3030; each component is coupled together through a bus system 3040. It can be understood that the bus system 3040 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 3040 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, various buses are labeled as bus systems 3040 in Figure 29. Among them,

[0350] Communication interface 3010, used for sending and receiving signals during the process of sending and receiving information with other external network elements;

[0351] Memory 3020, used for storing computer programs;

[0352] The processor 3030 is configured to, when running the computer program, execute:

[0353] Parsing a bitstream to determine first information for indicating a target decoding mode from a plurality of decoding modes corresponding to a current layer, the plurality of decoding modes comprising RAHT transform decoding and RAHT prediction combined with transform decoding;

[0354] Decode the attribute information of the nodes of the current layer according to the target decoding mode.

[0355] It is understood that the memory 3020 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 RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM). The memory 3020 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0356] The processor 3030 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by hardware integrated logic circuits or software instructions in the processor 3030. The processor 3030 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 modules can be located in storage media such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other well-known storage media in the art. The storage medium is located in the memory 3020, and the processor 3030 reads the information in the memory 3020 and completes the steps of the above method in combination with its hardware.

[0357] It is understood that the embodiments described herein can be implemented with hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein or a combination thereof. For software implementation, the technology described herein can be implemented by a module (such as a process, a function, etc.) that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in a processor or outside a processor.

[0358] Optionally, as another embodiment, the processor 3030 is further configured to execute the decoding method described in any one of the aforementioned embodiments when running the computer program.

[0359] FIG31 is a schematic diagram of the structure of an encoder provided by an embodiment of the present application. As shown in FIG31 , the encoder 3100 includes a first determining unit 3110 and a first encoding unit 3120 .

[0360] The first determining unit 3110 is configured to determine a target coding mode corresponding to the current layer from a plurality of coding modes, where the plurality of coding modes include RAHT transform coding and RAHT prediction combined with transform coding.

[0361] The first encoding unit 3120 is configured to encode the attribute information of the node of the current layer according to the target coding mode.

[0362] In some implementations, the RAHT prediction combined with transform coding includes at least one of the following: RAHT intra prediction combined with transform coding; RAHT inter prediction combined with transform coding; a combination of RAHT intra prediction combined with transform coding and RAHT inter prediction combined with transform coding.

[0363] In some implementations, the multiple coding modes include: RAHT transform coding; RAHT intra prediction combined with transform coding; a combination of RAHT intra prediction combined with transform coding and RAHT inter prediction combined with transform coding.

[0364] In some implementations, the multiple coding modes include: RAHT transform coding; RAHT intra-frame prediction combined with transform coding; RAHT inter-frame prediction combined with transform coding.

[0365] In some implementations, the encoder 3100 further includes: a second encoding unit configured to write first information into a bitstream, where the first information is used to indicate the target encoding mode.

[0366] In some implementations, the first information is included in an attribute block header.

[0367] In some implementations, the first determination unit 3110 is configured to determine whether the attribute information satisfies a condition for performing inter-frame prediction; if the attribute information satisfies the condition, determine the target encoding mode from the multiple encoding modes.

[0368] In some implementations, the encoder 3100 further includes: a third encoding unit configured to perform RAHT transform coding and / or RAHT intra-frame prediction combined with transform coding on the attribute information if the attribute information does not meet the condition.

[0369] In some implementations, the target coding mode is a combination of RAHT intra-frame prediction combined with transform coding and RAHT inter-frame prediction combined with transform coding, and the first coding unit 3120 is configured to determine the intra-frame prediction value and the inter-frame prediction value of the AC coefficient of the node in the current layer according to the target coding mode; fuse the intra-frame prediction value and the inter-frame prediction value to determine the target prediction value of the AC coefficient; and determine the reconstruction attribute information of the node in the current layer according to the target prediction value.

[0370] In some implementations, the multiple coding modes include RAHT inter-frame prediction combined with transform coding, and the reference frame of the RAHT inter-frame prediction combined with transform coding is determined from a first reference frame and a second reference frame, the first reference frame is a reference frame after motion compensation, and the second reference frame is a reference frame that has not undergone motion compensation.

[0371] In some implementations, the motion compensation is local motion compensation or global motion compensation.

[0372] In some implementations, the reference frame for the RAHT inter-frame prediction combined with transform coding is determined based on a rate-distortion cost corresponding to the first reference frame and a rate-distortion cost corresponding to the second reference frame.

[0373] In some implementations, the encoder 3100 further includes: a fourth encoding unit configured to write second information into the bitstream if the target coding mode is RAHT inter-frame prediction combined with transform coding, wherein the second information is used to indicate a reference frame corresponding to the RAHT inter-frame prediction combined with transform coding.

[0374] In some implementations, the encoder 3100 further includes: a fifth encoding unit configured to write third information into the bitstream, where the third information is used to indicate whether to use the reference frame after motion compensation for RAHT inter-frame prediction.

[0375] In some implementations, the third information corresponds to a slice or to a RAHT layer.

[0376] In some implementations, the target coding mode is determined based on rate-distortion costs of the multiple coding modes.

[0377] In some implementations, the target coding mode is a coding mode with the lowest rate-distortion cost among the multiple coding modes.

[0378] In some implementations, the rate-distortion cost of the target coding mode is determined based on a distortion parameter, a bit rate parameter, and a Lagrangian factor corresponding to the target coding mode, and the Lagrangian factor is determined based on a quantization parameter of the attribute information.

[0379] In some implementations, the first encoding unit 3120 is configured to determine a quantization coefficient based on an AC coefficient determined based on the target coding mode; and determine the coding bits corresponding to the attribute information based on the quantization coefficient.

[0380] In some implementations, the current layer is determined based on dividing a previous layer of the current layer at least once along an X direction, a Y direction, and a Z direction.

[0381] In some implementations, the RAHT prediction combined with transform coding includes RAHT inter-frame prediction combined with transform coding, and a reference frame of the RAHT inter-frame prediction combined with transform coding is a reference frame that has undergone global motion compensation or local motion compensation.

[0382] In some implementations, the encoding method is applied to GES-TM.

[0383] It is understandable that in the embodiments of the present application, a "unit" can be a portion of a circuit, a portion of a processor, a portion of a program or software, etc., and of course it can also be a module, or it can be non-modular. Moreover, the various components in this embodiment can be integrated into a processing unit, or each unit 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 in the form of software functional modules.

[0384] If the integrated unit is implemented as a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, or the portion that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in this embodiment. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0385] Therefore, an embodiment of the present application provides a computer-readable storage medium, which is applied to the encoder 3100. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the decoding method described in any one of the aforementioned embodiments.

[0386] Based on the composition of the above-mentioned encoder 3100 and the computer-readable storage medium, refer to Figure 32, which shows a specific hardware structure diagram of the encoder 3100 provided in an embodiment of the present application. As shown in Figure 32, the encoder 3200 may include: a communication interface 3210, a memory 3220 and a processor 3230; each component is coupled together through a bus system 3240. It can be understood that the bus system 3240 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 3240 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, various buses are labeled as bus systems 3240 in Figure 32. Among them,

[0387] Communication interface 3210, used for sending and receiving signals when sending and receiving information with other external network elements;

[0388] Memory 3220, for storing computer programs;

[0389] The processor 3230 is configured to, when running the computer program, execute:

[0390] Determining a target coding mode corresponding to a current layer from a plurality of coding modes, the plurality of coding modes comprising RAHT transform coding and RAHT prediction combined with transform coding;

[0391] The attribute information of the nodes of the current layer is encoded according to the target coding mode.

[0392] It will be appreciated that the memory 3220 in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be a ROM, PROM, EPROM, EEPROM, or flash memory. The volatile memory may be a RAM, which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as SRAM, DRAM, SDRAM, DDRSDRAM, ESDRAM, SLDRAM, and DRRAM. The memory 3220 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0393] Processor 3230 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by hardware integrated logic circuits or software instructions within processor 3230. The processor 3230 may be a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods 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 within the decoding processor. The software modules can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory 3220. Processor 3230 reads information from memory 3220 and, in conjunction with its hardware, completes the steps of the above method.

[0394] It is understood that the embodiments described herein can be implemented with hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more ASICs, DSPs, DSPDs, PLDs, FPGAs, general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or a combination thereof. For software implementation, the technology described herein can be implemented by modules (e.g., processes, functions, etc.) that perform the functions described herein. 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.

[0395] Optionally, as another embodiment, the processor 3230 is further configured to execute the encoding method described in any one of the aforementioned embodiments when running the computer program.

[0396] An embodiment of the present application also provides a computer-readable storage medium, which is a non-volatile computer-readable storage medium for storing a bit stream. The bit stream can be generated by an encoding method of an encoder, or the bit stream can be decoded by a decoding method of a decoder, wherein the decoding method can be the decoding method described in any of the foregoing embodiments, and the encoding method can be the encoding method described in any of the foregoing embodiments.

[0397] It should be noted that, in this application, 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.

[0398] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0399] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0400] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0401] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0402] The above description is merely a specific 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, comprising: Parsing a bitstream to determine first information, where the first information is used to indicate a target decoding mode from a plurality of decoding modes corresponding to a current layer, where the plurality of decoding modes include regional adaptive hierarchical transform (RAHT) transform decoding, and RAHT prediction combined with transform decoding; The attribute information of the nodes of the current layer is decoded according to the target decoding mode.

2. The method according to claim 1, wherein: The RAHT prediction combined with transform decoding includes at least one of the following: RAHT intra prediction combined with transform decoding; RAHT inter-frame prediction combined with transform decoding; A combination of RAHT intra prediction combined with transform decoding and RAHT inter prediction combined with transform decoding.

3. The method according to claim 1, wherein: The multiple decoding modes include: RAHT transform decoding; RAHT intra prediction combined with transform decoding; A combination of RAHT intra prediction combined with transform decoding and RAHT inter prediction combined with transform decoding.

4. The method according to claim 1, wherein: The multiple decoding modes include: RAHT transform decoding; RAHT intra prediction combined with transform decoding; RAHT inter-frame prediction combined with transform decoding.

5. The method according to claim 1, wherein: The parsing of the code stream to determine the first information includes: Determining whether the attribute information satisfies a condition for performing inter-frame prediction; If the attribute information satisfies the condition, the code stream is parsed to determine the first information.

6. The method according to claim 5, wherein: The method further comprises: If the attribute information does not satisfy the condition, RAHT transform decoding and / or RAHT intra prediction combined with transform decoding are performed on the attribute information.

7. The method according to claim 1, wherein: The target decoding mode is a combination of RAHT intra prediction combined with transform decoding and RAHT inter prediction combined with transform decoding, and the decoding of the attribute information of the node of the current layer according to the target decoding mode includes: Determine the intra-frame prediction value and the inter-frame prediction value of the AC coefficient of the node in the current layer according to the target decoding mode; Fusing the intra-frame prediction value and the inter-frame prediction value to determine a target prediction value of the AC coefficient; The reconstruction attribute information of the nodes in the current layer is determined according to the target prediction value.

8. The method according to claim 1, wherein: The multiple decoding modes include RAHT inter-frame prediction combined with transform decoding, and the reference frame of the RAHT inter-frame prediction combined with transform decoding is determined from a first reference frame and a second reference frame, the first reference frame is a reference frame after motion compensation, and the second reference frame is a reference frame that has not undergone motion compensation.

9. The method according to claim 8, wherein: The motion compensation is local motion compensation or global motion compensation.

10. The method according to claim 8, wherein: The method further comprises: If the target decoding mode is RAHT inter-frame prediction combined with transform decoding, the bitstream is parsed to determine second information, where the second information is used to indicate a reference frame corresponding to the RAHT inter-frame prediction combined with transform decoding.

11. The method according to claim 8, wherein: The method further comprises: The bitstream is parsed to determine third information, where the third information is used to indicate whether a reference frame after motion compensation will be used for RAHT inter-frame prediction.

12. The method according to claim 11, wherein: The third information corresponds to a slice or to a RAHT layer.

13. The method according to claim 1, wherein: Before decoding the attribute information of the node of the current layer according to the target decoding mode, the method further includes: Parsing the bitstream to determine the quantization coefficients of the nodes of the current layer; Dequantizing the quantized coefficients to determine AC coefficients of the nodes of the current layer; The decoding of the attribute information of the node of the current layer according to the target decoding mode includes: The AC coefficients are predicted and / or inversely transformed according to the target decoding mode to determine reconstruction attribute information of the nodes of the current layer.

14. The method according to claim 1, wherein: The current layer is determined based on dividing a previous layer of the current layer at least once along an X direction, a Y direction, and a Z direction.

15. The method according to claim 1, wherein: The first information is included in the attribute block header.

16. The method according to claim 1, wherein: The RAHT prediction combined with transform decoding includes RAHT inter-frame prediction combined with transform decoding, and a reference frame of the RAHT inter-frame prediction combined with transform decoding is a reference frame that has undergone global motion compensation or local motion compensation.

17. The method according to claim 1, wherein: The decoding method is applied to the Geometric Exploration Entity Test Model GES-TM.

18. A coding method, applied to an encoder, comprising: Determine a target coding mode corresponding to the current layer from a plurality of coding modes, wherein the plurality of coding modes include region adaptive hierarchical transform (RAHT) transform coding and RAHT prediction combined with transform coding; The attribute information of the nodes of the current layer is encoded according to the target coding mode.

19. The method according to claim 18, wherein: The RAHT prediction combined with transform coding includes at least one of the following: RAHT intra prediction combined with transform coding; RAHT inter-frame prediction combined with transform coding; A combination of RAHT intra prediction combined with transform coding and RAHT inter prediction combined with transform coding.

20. The method according to claim 18, wherein: The multiple encoding modes include: RAHT transform coding; RAHT intra prediction combined with transform coding; A combination of RAHT intra prediction combined with transform coding and RAHT inter prediction combined with transform coding.

21. The method according to claim 18, wherein: The multiple encoding modes include: RAHT transform coding; RAHT intra prediction combined with transform coding; RAHT inter-frame prediction combined with transform coding.

22. The method according to claim 18, wherein: The method further comprises: The first information is written into a bitstream, where the first information is used to indicate the target coding mode.

23. The method according to claim 22, wherein: The first information is included in the attribute block header.

24. The method of claim 18, wherein: The determining a target coding mode corresponding to the current layer from a plurality of coding modes includes: Determining whether the attribute information satisfies a condition for performing inter-frame prediction; If the attribute information satisfies the condition, the target encoding mode is determined from among the plurality of encoding modes.

25. The method according to claim 24, wherein: The method further comprises: If the attribute information does not satisfy the condition, RAHT transform coding and / or RAHT intra prediction combined with transform coding are performed on the attribute information.

26. The method of claim 18, wherein: The target coding mode is a combination of RAHT intra prediction combined with transform coding and RAHT inter prediction combined with transform coding, and encoding the attribute information of the node of the current layer according to the target coding mode includes: Determine the intra-frame prediction value and the inter-frame prediction value of the AC coefficient of the node in the current layer according to the target coding mode; Fusing the intra-frame prediction value and the inter-frame prediction value to determine a target prediction value of the AC coefficient; The reconstruction attribute information of the nodes in the current layer is determined according to the target prediction value.

27. The method according to claim 18, wherein: The multiple encoding modes include RAHT inter-frame prediction combined with transform coding, and the reference frame of the RAHT inter-frame prediction combined with transform coding is determined from a first reference frame and a second reference frame, the first reference frame is a reference frame after motion compensation, and the second reference frame is a reference frame that has not undergone motion compensation.

28. The method according to claim 27, wherein: The motion compensation is local motion compensation or global motion compensation.

29. The method according to claim 27, wherein: The reference frame for the RAHT inter-frame prediction combined with transform coding is determined based on a rate-distortion cost corresponding to the first reference frame and a rate-distortion cost corresponding to the second reference frame.

30. The method of claim 27, wherein: The method further comprises: If the target coding mode is RAHT inter-frame prediction combined with transform coding, second information is written into the bitstream, where the second information is used to indicate a reference frame corresponding to the RAHT inter-frame prediction combined with transform coding.

31. The method of claim 27, wherein: The method further comprises: The third information is written into the bitstream, where the third information is used to indicate whether the reference frame after motion compensation will be used for RAHT inter-frame prediction.

32. The method according to claim 31, wherein: The third information corresponds to a slice or to a RAHT layer.

33. The method of claim 18, wherein: The target coding mode is determined based on rate-distortion costs of the multiple coding modes.

34. The method of claim 33, wherein: The target coding mode is a coding mode with the lowest rate-distortion cost among the multiple coding modes.

35. The method of claim 33, wherein: The rate-distortion cost of the target coding mode is determined based on a distortion parameter, a code rate parameter and a Lagrangian factor corresponding to the target coding mode, and the Lagrangian factor is determined based on a quantization parameter of the attribute information.

36. The method of claim 18, wherein: The encoding of the attribute information of the node of the current layer according to the target coding mode includes: Determining a quantization coefficient according to an AC coefficient determined based on the target coding mode; The coding bits corresponding to the attribute information are determined according to the quantization coefficient.

37. The method of claim 18, wherein: The current layer is determined based on dividing a previous layer of the current layer at least once along an X direction, a Y direction, and a Z direction.

38. The method of claim 18, wherein: The RAHT prediction combined with transform coding includes RAHT inter-frame prediction combined with transform coding, and a reference frame of the RAHT inter-frame prediction combined with transform coding is a reference frame that has undergone global motion compensation or local motion compensation.

39. The method of claim 18, wherein: The encoding method is applied to the geometric exploration entity test model GES-TM.

40. A decoder comprising: A first decoding unit is configured to parse a bitstream and determine first information, where the first information is used to indicate a target decoding mode from a plurality of decoding modes corresponding to a current layer, where the plurality of decoding modes include RAHT transform decoding and RAHT prediction combined transform decoding; The second decoding unit is configured to decode the attribute information of the node of the current layer according to the target decoding mode.

41. A decoder, the decoder comprising: Memory for storing computer programs; A processor, configured to execute the method according to any one of claims 1 to 17 when running the computer program.

42. An encoder comprising: A first determining unit is configured to determine a target coding mode corresponding to a current layer from a plurality of coding modes, wherein the plurality of coding modes include RAHT transform coding and RAHT prediction combined with transform coding; The first encoding unit is configured to encode the attribute information of the node of the current layer according to the target encoding mode.

43. An encoder, comprising: Memory for storing computer programs; A processor, configured to execute the method according to any one of claims 18 to 39 when running the computer program.

44. 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 17 or the method according to any one of claims 18 to 39 is implemented.

45. A non-volatile computer-readable storage medium storing a bit stream, wherein the bit stream is generated by an encoding method using an encoder, or the bit stream is decoded by a decoding method using a decoder, wherein: The decoding method is the method according to any one of claims 1 to 17, and the encoding method is the method according to any one of claims 18 to 39.

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