Point cloud encoding processing method, point cloud decoding processing method and related devices

Abstract

The present application discloses a point cloud encoding processing method, a point cloud decoding processing method and related devices, belonging to the technical field of point cloud processing. The point cloud encoding processing method of the embodiments of the present application includes: performing predictive encoding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information; quantizing the geometric prediction residual information according to geometric quantization parameters to obtain quantized geometric prediction residual information; and performing entropy encoding based on the quantized geometric prediction residual information to obtain a geometric bitstream.

Description

Technical Field

[0001] This application belongs to the technical field of point cloud processing, and specifically relates to a point cloud encoding processing method, a point cloud decoding processing method, and related devices. Background Art

[0002] A point cloud is a form of representation of a three-dimensional object or scene, and is composed of a set of discrete points that are irregularly distributed in space and express the spatial structure and surface attributes of the three-dimensional object or scene. In order to accurately reflect the information in space, the number of discrete points required is quite large. In order to reduce the bandwidth occupied during the storage and transmission of point cloud data, it is necessary to perform encoding and compression processing on the point cloud data. Point cloud data usually consists of geometric information describing positions such as three-dimensional coordinates (x, y, z) and attribute information at that position such as color (R, G, B) or reflectivity, etc. During the point cloud encoding and compression process, the encoding of geometric information and attribute information is carried out separately.

[0003] Currently, before encoding the geometric information of a point cloud, preprocessing such as rounding and duplicate removal is performed on the geometric information, and the source density has a greater impact on the preprocessing of geometric information. For example, when the source distribution is relatively dense, preprocessing the geometric information will sharply reduce the number of points in the point cloud, resulting in the rate of the geometric bitstream being greatly affected by the source density, thus resulting in a poor rate control effect for the geometric bitstream of the point cloud. Summary of the Invention

[0004] Embodiments of this application provide a point cloud encoding processing method, a point cloud decoding processing method, and related devices, which can solve the problem that the rate of the geometric bitstream is greatly affected by the source density, thus resulting in a poor rate control effect for the geometric bitstream of the point cloud.

[0005] In a first aspect, a point cloud encoding processing method is provided, and the method includes:

[0006] Performing predictive encoding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information;

[0007] Quantizing the geometric prediction residual information according to a geometric quantization parameter to obtain quantized geometric prediction residual information;

[0008] Performing entropy encoding based on the quantized geometric prediction residual information to obtain a geometric bitstream.

[0009] In a second aspect, a point cloud decoding processing method is provided, and the method includes:

[0010] Performing entropy decoding on the geometric bitstream to obtain quantized geometric prediction residual information;

[0011] Inverse-quantize the quantized geometric prediction residual information according to geometric quantization parameters to obtain geometric prediction residual information;

[0012] Perform prediction decoding based on the geometric prediction residual information to obtain the geometric information of the point cloud to be decoded.

[0013] In a third aspect, a point cloud encoding processing apparatus is provided, including:

[0014] A first encoding module, configured to perform prediction encoding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information;

[0015] A first quantization module, configured to quantize the geometric prediction residual information according to geometric quantization parameters to obtain quantized geometric prediction residual information;

[0016] A second encoding module, configured to perform entropy encoding based on the quantized geometric prediction residual information to obtain a geometric code stream.

[0017] In a fourth aspect, a point cloud decoding processing apparatus is provided, including:

[0018] A first decoding module, configured to perform entropy decoding on the geometric code stream to obtain quantized geometric prediction residual information;

[0019] A first inverse quantization module, configured to inverse-quantize the quantized geometric prediction residual information according to geometric quantization parameters to obtain geometric prediction residual information;

[0020] A second decoding module, configured to perform prediction decoding based on the geometric prediction residual information to obtain the geometric information of the point cloud to be decoded.

[0021] In a fifth aspect, a terminal is provided, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented; or, when the program or instruction is executed by the processor, the steps of the method described in the second aspect are implemented.

[0022] In a sixth aspect, a terminal is provided, including a processor and a communication interface. Wherein, the processor or the communication interface is used for:

[0023] Perform prediction encoding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information;

[0024] Quantize the geometric prediction residual information according to geometric quantization parameters to obtain quantized geometric prediction residual information;

[0025] Perform entropy encoding based on the quantized geometric prediction residual information to obtain a geometric code stream.

[0026] In a seventh aspect, a terminal is provided, including a processor and a communication interface, wherein the processor or the communication interface is configured to:

[0027] Perform entropy decoding on a geometric bitstream to obtain quantized geometric prediction residual information;

[0028] Perform inverse quantization processing on the quantized geometric prediction residual information according to a geometric quantization parameter to obtain geometric prediction residual information;

[0029] Perform prediction decoding based on the geometric prediction residual information to obtain geometric information of a point cloud to be decoded.

[0030] In an eighth aspect, a readable storage medium is provided, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the point cloud encoding processing method described in the first aspect are implemented, or when the program or instructions are executed by a processor, the steps of the point cloud decoding processing method described in the second aspect are implemented.

[0031] In a ninth aspect, a chip is provided, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or instructions to implement the steps of the point cloud encoding processing method described in the first aspect, or to implement the steps of the point cloud decoding processing method described in the second aspect.

[0032] In a tenth aspect, a computer program / program product is provided, the computer program / program product is stored in a non-volatile storage medium, and the program / program product is executed by at least one processor to implement the steps of the point cloud encoding processing method described in the first aspect, or to implement the steps of the point cloud decoding processing method described in the second aspect..

[0033] In the embodiments of the present application, prediction encoding is performed based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information; quantization processing is performed on the geometric prediction residual information according to a geometric quantization parameter to obtain quantized geometric prediction residual information; entropy encoding is performed based on the quantized geometric prediction residual information to obtain a geometric bitstream. In this way, by performing quantization processing on the geometric prediction residual information through the geometric quantization parameter, the influence of the source density on the geometric bitstream rate is reduced, and the rate control effect of the geometric bitstream of the point cloud can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is one of the schematic diagrams of a point cloud AVS encoder framework;

[0035] Figure 2 is one of the schematic diagrams of a point cloud AVS decoder framework;

[0036] Figure 3 It is a flowchart of a point cloud encoding processing method provided by an embodiment of the present application;

[0037] Figure 4 It is the second schematic diagram of a point cloud AVS encoder framework;

[0038] Figure 5 It is the second schematic diagram of a point cloud AVS decoder framework;

[0039] Figure 6 It is a flowchart of a point cloud decoding processing method provided by an embodiment of the present application;

[0040] Figure 7 It is the first structure diagram of a point cloud encoding processing device provided by an embodiment of the present application;

[0041] Figure 8 It is the second structure diagram of a point cloud encoding processing device provided by an embodiment of the present application;

[0042] Figure 9 It is the third structure diagram of a point cloud encoding processing device provided by an embodiment of the present application;

[0043] Figure 10 It is the fourth structure diagram of a point cloud encoding processing device provided by an embodiment of the present application;

[0044] Figure 11 It is the first structure diagram of a point cloud decoding processing device provided by an embodiment of the present application;

[0045] Figure 12 It is the second structure diagram of a point cloud decoding processing device provided by an embodiment of the present application;

[0046] Figure 13 It is the structure diagram of a communication device provided by an embodiment of the present application;

[0047] Figure 14 It is the structure diagram of a terminal provided by an embodiment of the present application. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0049] The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0050] The encoding / decoding end corresponding to the encoding / decoding method in the embodiments of this application can be a terminal, which can also be referred to as a terminal device or a user terminal (User Equipment, UE). The terminal can be a mobile phone, a tablet personal computer, a laptop computer or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile Internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device or a vehicle-mounted device (VUE), a pedestrian terminal (PUE), etc. Terminal-side devices. Wearable devices include smart watches, bracelets, earphones, glasses, etc. It should be noted that the specific type of the terminal is not limited in the embodiments of this application.

[0051] For ease of understanding, some content related to the embodiments of this application is described below:

[0052] Such as Figure 1As shown in the figure, in the encoder framework of the Point Cloud Audio Video Coding Standard (AVS), the geometric information and attribute information of the point cloud are encoded separately. First, the coordinate transformation is performed on the geometric information to make the point cloud fully contained in a bounding box, and then the coordinate quantization is carried out. Quantization mainly plays a role in scaling. Since quantization rounds the geometric coordinates, the geometric information of some points is the same, which are called duplicate points. Whether to remove duplicate points is determined according to parameters. These two steps of quantization and removing duplicate points are also called the voxelization process. Next, the bounding box is divided into a multi-way tree, such as an octree, a quadtree, or a binary tree. In the geometric information coding framework based on the multi-way tree, the bounding box is equally divided into 8 sub-cubes, and the non-empty sub-cubes are continuously divided until the division reaches the leaf node of a 1x1x1 unit cube and then the division stops. The number of points in the leaf node is encoded to generate a binary bitstream. Currently, there are two types of AVS geometric division orders:

[0053] 1. Breadth-first traversal order: When performing octree division on the geometry, first divide the nodes at the current same layer until all the nodes at the current layer are divided, and then continue to divide the nodes at the next layer. Finally, the division stops when the obtained leaf node is a 1x1x1 unit cube.

[0054] 2. Depth-first traversal order: When performing octree division on the geometry, first continuously divide the first node at the current layer until the division reaches the leaf node of a 1x1x1 unit cube and then stop dividing the current node. According to this order, divide the subsequent nodes at the current layer until all the nodes at the current layer are divided and stop.

[0055] After the geometric coding is completed, the geometric information is reconstructed for subsequent recoloring. The attribute coding mainly targets color and reflectivity information. First, it is judged whether to perform color space conversion according to parameters. If color space conversion is performed, the color information is converted from the Red Green Blue (RGB) color space to the Luminance-Chrominance (YUV) color space. Then, the original point cloud is used to recolor the geometrically reconstructed point cloud so that the uncoded attribute information corresponds to the reconstructed geometric information. In the color information coding, after sorting the point cloud by Morton code, the nearest neighbor of the point to be predicted is searched using the geometric space relationship, and the predicted attribute value of the point to be predicted is obtained by using the reconstructed attribute value of the found neighbor. Then, the difference between the true attribute value and the predicted attribute value is obtained to get the prediction residual. Finally, the prediction residual is quantized and encoded to generate a binary bitstream.

[0056] Optionally, the AVS decoding process corresponds to the encoding process. Specifically, the AVS decoder framework is asFigure 2 shown.

[0057] It should be noted that the AVS coding framework may include two stages: preprocessing and coding. The processing of the point cloud in the preprocessing stage can be called out-of-loop processing, and after the preprocessing is completed, that is, the processing of the point cloud in the coding stage can be called in-loop processing.

[0058] The point cloud coding processing method provided in the embodiment of the present application is described in detail below through some embodiments and their application scenarios in combination with the accompanying drawings.

[0059] See also Figure 3 , Figure 3 is a flow chart of a point cloud coding processing method provided in an embodiment of the present application, such as Figure 3 As shown, the point cloud coding processing method includes the following steps:

[0060] Step 101: Perform predictive coding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information.

[0061] Among them, the geometric information may include geometric position. When performing predictive coding, a prediction candidate list can be established for the geometric information of the point cloud to be encoded, and the best geometric prediction value can be selected from the prediction candidate list, and the best geometric prediction value can be subtracted from the geometric information to obtain geometric prediction residual information. Each geometric prediction value in the prediction candidate list can correspond to a geometric prediction mode. For example, a prediction candidate list can be established in advance, and the prediction candidate list can include N geometric prediction values, wherein the N geometric prediction values ​​correspond to N geometric prediction modes one by one, and N is a positive integer greater than 1. Exemplarily, if the number of N is 4, that is, the prediction candidate list includes 4 geometric prediction values, and the point cloud to be encoded is the 5th point cloud to be encoded among all point clouds, then the geometric information of the 4 point clouds to be encoded with a coding order of 1 to 4 located before the point cloud to be encoded can be used to determine the geometric prediction value. For example, the geometric prediction value determination rule may be that the first geometric prediction value is the sum of the geometric information of the four point clouds to be encoded; the second geometric prediction value is the minimum geometric information of the four point clouds to be encoded; the third geometric prediction value is the average of the geometric information of the four point clouds to be encoded; and the fourth geometric prediction value is the difference between the geometric information of the fourth point cloud to be encoded and the geometric information of the third point cloud to be encoded. The geometric information of the point cloud to be encoded may be represented as the three-dimensional coordinates (x, y, z) of the point cloud to be encoded.

[0062] It should be understood that the specific determination rules for the geometric prediction values ​​can be set flexibly, and this embodiment does not make any specific limitations here.

[0063] Step 102: quantize the geometric prediction residual information according to the geometric quantization parameter to obtain quantized geometric prediction residual information.

[0064] Among them, it can be determined whether the geometric quantization control parameter indicates enabling quantization processing; in the case that the geometric quantization control parameter indicates enabling quantization processing, the geometric prediction residual information is quantized according to the geometric quantization parameter to obtain quantized geometric prediction residual information, and entropy coding is performed based on the quantized geometric prediction residual information to obtain a geometric bitstream; in the case that the geometric quantization control parameter indicates disabling quantization processing, entropy coding is performed based on the geometric prediction residual information to obtain a geometric bitstream.

[0065] In addition, the geometric quantization parameter can be read from a configuration file. By way of example, the value of the parameter GeomQP in the configuration file can be read as the geometric quantization parameter.

[0066] Step 103: Perform entropy coding based on the quantized geometric prediction residual information to obtain a geometric bitstream.

[0067] Among them, entropy coding can be performed on the quantized geometric prediction residual information and the geometric prediction mode to obtain a geometric bitstream.

[0068] It should be noted that in the geometric coding scheme based on an octree, a process of lossy quantization of geometric information is performed in preprocessing. In preprocessing, lossy quantization of removing duplicates and rounding the original point cloud data is performed through a quantization step. Not only is the original point cloud data rounded, but duplicate points are removed after rounding to achieve lossy quantization. The following problems exist in this lossy quantization in preprocessing: when the source distribution is relatively sparse and uniform, different quantization steps can be set to uniformly reduce the number of points after quantization, but when the source distribution is relatively dense and concentrated, a smaller quantization step will cause a sharp reduction in the number of points; lossy quantization is completed outside the loop and is achieved through the quantization step, which can be considered as a downsampling operation on the original point cloud data. The number of downsampled points is affected by the source, so the geometric bitstream after quantization is also affected by the source. At the same time, when the number of points is fixed, further lossy quantization of the point coordinates cannot be performed, so the rate control of the geometric bitstream cannot be achieved; the point cloud after preprocessing is greatly affected by the density of the source. In the case where the source is unknown, the quality of the geometric information of the point cloud after preprocessing cannot be accurately controlled by adjusting the quantization step outside the loop.

[0069] In the embodiment of the present application, the geometric prediction residual information is quantized through the geometric quantization parameter, and in-loop lossy quantization is introduced in point cloud coding, reducing the influence of the source density on the rate of the geometric bitstream and being able to improve the rate control effect of the geometric bitstream of the point cloud.

[0070] In an embodiment of the present application, predictive coding is performed based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information; the geometric prediction residual information is quantized according to geometric quantization parameters to obtain quantized geometric prediction residual information; entropy coding is performed based on the quantized geometric prediction residual information to obtain a geometric bitstream. In this way, by quantizing the geometric prediction residual information with geometric quantization parameters, the influence of the source density on the geometric bitstream rate is reduced, and the rate control effect of the geometric bitstream of the point cloud can be improved.

[0071] Optionally, the quantizing the geometric prediction residual information according to the geometric quantization parameters includes:

[0072] Determine whether the geometric quantization control parameter indicates enabling quantization processing;

[0073] When the geometric quantization control parameter indicates enabling quantization processing, the geometric prediction residual information is quantized according to the geometric quantization parameters.

[0074] Among them, when the geometric quantization control parameter indicates enabling quantization processing, it can be considered as indicating enabling in-loop lossy quantization for the geometric prediction residual information. The geometric quantization control parameter can be read from a configuration file. Exemplarily, the value of the parameter geometry_enable_quantizated_flag in the configuration file can be read as the geometric quantization control parameter. The parameter geometry_enable_quantizated_flag can be a newly introduced parameter in the gps (geometry parameters set) high-level syntax element. When the geometric quantization control parameter is configured to 1, it can indicate enabling quantization processing; when the geometric quantization control parameter is configured to 0, it can indicate not enabling quantization processing.

[0075] In this embodiment, by determining whether to quantize the geometric prediction residual information according to the geometric quantization parameters through the geometric quantization control parameter, the flexibility of the geometric information coding of the point cloud can be improved.

[0076] Optionally, after determining whether the geometric quantization control parameter indicates enabling quantization processing, the method further includes:

[0077] When the geometric quantization control parameter indicates not enabling quantization processing, entropy coding is performed based on the geometric prediction residual information to obtain a geometric bitstream.

[0078] Among them, when the geometric quantization control parameter indicates not enabling quantization processing, it can be considered as indicating not enabling in-loop lossy quantization for the geometric prediction residual information.

[0079] In this embodiment, when the geometric quantization control parameter indicates that quantization processing is not enabled, entropy coding is performed based on the geometric prediction residual information, so that it is possible to determine whether to perform quantization processing on the geometric prediction residual information during the predictive coding of geometric information according to the geometric quantization control parameter, which can improve the flexibility of geometric information coding of point clouds.

[0080] Optionally, the predictive coding of the geometric information of the point cloud to be coded includes:

[0081] Dividing the point cloud to be coded into a first sub-point cloud to be coded and a second sub-point cloud to be coded based on the node identifier corresponding to the point cloud to be coded;

[0082] When the geometric coding control parameter indicates the first coding mode, predictive coding is performed on the geometric information of the first sub-point cloud to be coded;

[0083] When the geometric coding control parameter indicates the second coding mode, predictive coding is performed on the geometric information of the second sub-point cloud to be coded.

[0084] Among them, the point cloud to be coded can be divided into a first sub-point cloud to be coded and a second sub-point cloud to be coded according to the relationship between the node identifier corresponding to the point cloud to be coded and a preset threshold. The first sub-point cloud to be coded can be a low-bit point cloud to be coded, and the second sub-point cloud to be coded can be a high-bit point cloud to be coded. The geometric information of the high-bit point cloud to be coded can include octree high-bit coordinates, and the geometric information of the low-bit point cloud to be coded can include octree low-bit coordinates.

[0085] Exemplarily, as Figure 4 shown, for the high-bit point cloud to be coded, octree construction is performed to achieve octree coding; for the low-bit point cloud to be coded, geometric prediction and residual quantization are performed to achieve predictive coding. And at the decoding end, as Figure 5 shown, the high-bit point cloud to be coded is obtained through octree reconstruction; the low-bit point cloud to be coded is obtained through inverse quantization and geometric reconstruction.

[0086] Taking the geometric information of the point cloud to be coded represented by Morton code and constructing a geometric octree for the geometric information through Morton code as an example, the node identifier corresponding to the point cloud to be coded can be the number of coding layers in the octree coding process. Exemplarily, the preset threshold can be 5, and all the point clouds to be coded can include 10 coding layers. The point clouds corresponding to the 1st coding layer to the 4th coding layer can be used as the high-bit point clouds to be coded, and the point clouds corresponding to the 5th coding layer to the 10th coding layer can be used as the low-bit point clouds to be coded.

[0087] In addition, the preset threshold can be the value of the parameter octree_division_end_nodeSizeLog2[3]. The value of the parameter octree_division_end_nodeSizeLog2[3] can be read from the configuration file as the preset threshold. When the geometric quantization parameter is greater than or equal to the preset threshold, the geometric prediction residual information is quantized to 0, and entropy coding may not be required for the quantized geometric prediction residual information. In the first coding mode and when the preset threshold matches the first quantization parameter of the out-of-loop quantization in the preprocessing, the lossy quantization of the point cloud is consistent with the existing quantization. When the geometric quantization parameter is less than the preset threshold, the geometric prediction residual information is not quantized to 0, and entropy coding can be performed based on the quantized geometric prediction residual information.

[0088] In this embodiment, when the geometric coding control parameter indicates different coding modes, the point cloud to be predicted-coded for geometric information is different. The user can modify the coding mode by setting the geometric coding control parameter, thereby modifying the coding method of the point cloud to be coded, and thus improving the flexibility of point cloud coding.

[0089] Optionally, the entropy coding based on the quantized geometric prediction residual information includes:

[0090] Determining at least two candidate geometric prediction residual information based on the quantized geometric prediction residual information;

[0091] Obtaining the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information;

[0092] Determining the target quantized geometric prediction residual information according to the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information;

[0093] Performing entropy coding based on the target quantized geometric prediction residual information.

[0094] Among them, the at least two candidate geometric prediction residual information may include candidate geometric prediction residual information related to the quantized geometric prediction residual information and candidate geometric prediction residual information unrelated to the quantized geometric prediction residual information. Exemplarily, the at least two candidate geometric prediction residual information may include the quantized geometric prediction residual information and the fixed value {0, 0, 0}.

[0095] In this embodiment, for the quantized geometric prediction residual information, a rate-distortion optimization algorithm is introduced to process and obtain the target quantized geometric prediction residual information, and entropy coding is performed based on the target quantized geometric prediction residual information, which can improve the efficiency of lossy coding of geometric information.

[0096] Optionally, the target quantized geometric prediction residual information is the candidate geometric prediction residual information with the minimum rate - distortion cost among the at least two candidate geometric prediction residual information.

[0097] Among them, the at least two candidate geometric prediction residual information can be stored in the form of a candidate list, and the first candidate geometric prediction residual information in the candidate list is used as the best candidate geometric prediction residual information; traverse the candidate geometric prediction residual information in the candidate list; if the rate - distortion cost corresponding to the current candidate geometric prediction residual information is less than the rate - distortion cost corresponding to the best candidate geometric prediction residual information, then update the current candidate geometric prediction residual information to the best candidate geometric prediction residual information, otherwise, do not update the best candidate geometric prediction residual information; after traversing the candidate list, determine the best candidate geometric prediction residual information as the target quantized geometric prediction residual information. After determining the target quantized geometric prediction residual information, the target quantized geometric prediction residual information can be input into the encoder for entropy coding.

[0098] In this embodiment, the candidate geometric prediction residual information with the minimum rate - distortion cost among the at least two candidate geometric prediction residual information is determined as the target quantized geometric prediction residual information, so as to optimize the lossy coding process of geometric information and improve the point cloud coding efficiency.

[0099] Optionally, the rate - distortion cost corresponding to the candidate geometric prediction residual information is determined based on the geometric distortion value and the first prediction residual bitrate. The geometric distortion value is used to characterize the geometric distortion corresponding to the candidate geometric prediction residual information, and the first prediction residual bitrate is used to characterize the expected number of bits for coding the candidate geometric prediction residual information.

[0100] Among them, the rate - distortion cost corresponding to the candidate geometric prediction residual information may be positively correlated with both the geometric distortion value and the first prediction residual bitrate. Exemplarily, the rate - distortion cost cost1 corresponding to the candidate geometric prediction residual information may be:

[0101] cost1 = dist1+λ1*rate1

[0102] Among them, λ1 can represent the weight parameter of the bitrate and the distortion in the rate - distortion cost. Exemplarily, λ1 can be set to 0.4, 0.5 or 0.6, etc.; rate1 can represent the first prediction residual bitrate; dist can represent the geometric distortion value. The calculation formula of the geometric distortion value dist1 can be as follows:

[0103] dist1 = normal1(recPos - oriPos)

[0104] Among them, the function normal1 represents calculating the first norm of an expression, recPos represents the geometric coordinates reconstructed using the candidate geometric prediction residual information and the geometric prediction value, and oriPos represents the original geometric coordinates.

[0105] In this embodiment, the rate-distortion cost corresponding to the candidate geometric prediction residual information is determined based on the geometric distortion value and the first prediction residual bit rate, and can more accurately determine the rate-distortion cost corresponding to the candidate geometric prediction residual information.

[0106] Optionally, among the at least two candidate geometric prediction residual information, there is candidate geometric prediction residual information related to the quantized geometric prediction residual information and candidate geometric prediction residual information unrelated to the quantized geometric prediction residual information;

[0107] The entropy coding based on the target quantized geometric prediction residual information includes:

[0108] When the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information;

[0109] When the target quantized geometric prediction residual information is candidate geometric prediction residual information unrelated to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information.

[0110] Among them, each candidate geometric prediction residual information can be correspondingly set with an identifier. That the candidate geometric prediction residual information is related to the quantized geometric prediction residual information can be that the candidate geometric prediction residual information can be obtained based on the quantized geometric prediction residual information. For example, the candidate geometric prediction residual information is equal to the quantized geometric prediction residual information, and the identifier corresponding to this candidate geometric prediction residual information can be 1; or the candidate geometric prediction residual information is an integer multiple of the quantized geometric prediction residual information, and the identifier corresponding to this candidate geometric prediction residual information can be 2, and so on; that the candidate geometric prediction residual information is unrelated to the quantized geometric prediction residual information can be that the candidate geometric prediction residual information is preset geometric prediction residual information. For example, it can be (0, 0, 0), and the identifier corresponding to this candidate geometric prediction residual information can be 0.

[0111] In addition, a geometric rate distortion optimization control parameter can be set. If the geometric rate distortion optimization control parameter is a first preset value, then the entropy coding based on the target quantized geometric prediction residual information includes: when the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, performing entropy coding based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information; when the target quantized geometric prediction residual information is candidate geometric prediction residual information not related to the quantized geometric prediction residual information, performing entropy coding based on the identifier corresponding to the target quantized geometric prediction residual information.

[0112] If the geometric rate distortion optimization control parameter is a second preset value, then the entropy coding based on the target quantized geometric prediction residual information includes: when the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, performing entropy coding based on the target quantized geometric prediction residual information; when the target quantized geometric prediction residual information is candidate geometric prediction residual information not related to the quantized geometric prediction residual information, performing entropy coding based on the target quantized geometric prediction residual information.

[0113] In this embodiment, the first preset value and the second preset value are not limited. By way of example, the first preset value can be 1 and the second preset value can be 0.

[0114] Furthermore, it is possible to determine whether the target quantized geometric prediction residual information is related or not related to the quantized geometric prediction residual information through the identifier corresponding to the target quantized geometric prediction residual information. When decoding at the decoding end, the identifier corresponding to the target quantized geometric prediction residual information can be parsed first. If it is determined according to the identifier corresponding to the target quantized geometric prediction residual information that the target quantized geometric prediction residual information is not related to the quantized geometric prediction residual information, the target quantized geometric prediction residual information can be found according to the identifier corresponding to the target quantized geometric prediction residual information; if it is determined according to the identifier corresponding to the target quantized geometric prediction residual information that the target quantized geometric prediction residual information is related to the quantized geometric prediction residual information, the target quantized geometric prediction residual information can be decoded from the geometric bitstream.

[0115] In this embodiment, when the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information; when the target quantized geometric prediction residual information is candidate geometric prediction residual information not related to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information. In this way, for some target quantized geometric prediction residual information, it is not necessary to encode it, but only the identifier corresponding to the target quantized geometric prediction residual information is encoded, which can further improve the coding efficiency.

[0116] Optionally, the predictive coding of the geometric information of the point cloud to be encoded includes:

[0117] Obtaining a quantized point cloud corresponding to the point cloud to be encoded according to a preset first quantization step size;

[0118] Performing a duplicate removal process on the quantized point cloud;

[0119] Performing predictive coding on the geometric information of the point cloud to be encoded corresponding to the quantized point cloud obtained after the duplicate removal process.

[0120] Among them, before obtaining the quantized point cloud corresponding to the point cloud to be encoded according to the preset first quantization step size, as Figure 4 shown, coordinate translation processing can be performed on the point cloud to be encoded. Coordinate translation processing can move the bounding box to the coordinate origin (0, 0, 0), where the bounding box represents the smallest cuboid containing all points in the input point cloud. The first quantization step size can be preset by the user. Exemplarily, the first quantization step size QS can be:

[0121] QS = 2 i

[0122] Among them, in the lossless case, i = 0, and in the lossy case, according to different quantization levels (r01,..., r06), i = 9, 8, 6, 5, 3, 2, that is, the value of QS can be 512, 256, 64, 32, 8, 4, 1.

[0123] The method for obtaining the quantized point cloud corresponding to the point cloud to be encoded can be as follows:

[0124] X = round(x / QS)

[0125] Y = round(y / QS)

[0126] Z = round(z / QS)

[0127] Among them, (X, Y, Z) represents the quantization coordinates of the quantized point cloud, (x, y, z) represents the coordinates of the original point cloud, QS represents the first quantization step, and the round(s) function represents returning the integer closest to s. The specific definition of this function can be shown as follows:

[0128]

[0129] After calculating the quantization coordinates of the quantized point cloud, there will be cases where the quantization coordinates of multiple original point clouds are the same. The original point clouds with the same quantization coordinates are duplicate points. The quantized point cloud can be de-duplicated under the condition that the de-duplication parameter geom_remove_dup_flag indicates removing duplicate points. The coordinates of the point cloud to be encoded corresponding to the quantized point cloud obtained after de-duplication are the original point cloud coordinates, and no quantization operation is performed on the point cloud coordinates.

[0130] In this embodiment, predictive coding is performed on the geometric information of the point cloud to be encoded corresponding to the quantized point cloud obtained after de-duplication, so that only the number of point clouds is downsampled during out-of-loop quantization, and the point cloud coordinates are not quantized.

[0131] Optionally, the quantization process of the geometric prediction residual information according to the geometric quantization parameter includes:

[0132] Determine the first geometric quantization step according to the geometric quantization parameter;

[0133] Quantize the geometric prediction residual information based on the first geometric quantization step and the first preset geometric offset value.

[0134] Among them, the first geometric quantization step QS1 can be:

[0135]

[0136] Among them, 2 shift1 can represent the first preset geometric offset value, shift1 can represent the number of bits of offset during the quantization process, the larger shift1 is, the more accurate the quantization result is, and QP1 can represent the geometric quantization parameter.

[0137] Exemplarily, shift1 can be configured as 14.

[0138] The quantized geometric prediction residual information QtRes1 obtained by quantization processing can be:

[0139]

[0140] Among them, Res1 can represent the geometric prediction residual information, and offset1 can represent half of the first preset geometric offset value, that is, offset1 is 2 shift1-1, the rounding operation can be achieved through offset1.

[0141] In this embodiment, the first geometric quantization step is determined according to the geometric quantization parameter, and the geometric prediction residual information is quantized based on the first geometric quantization step and the first preset geometric offset value, so as to obtain a better quantization effect.

[0142] Optionally, the geometric prediction residual information includes sub-geometric prediction residual information in three dimensions;

[0143] The geometric quantization parameter includes three sub-geometric quantization parameters respectively corresponding to the sub-geometric prediction residual information in the three dimensions.

[0144] Among them, the three dimensions can be the X, Y, and Z dimensions in the three-dimensional coordinate system respectively. The three sub-geometric quantization parameters can be configured through the parameter GeomQP[3] in the configuration file (cfg), and the three sub-geometric quantization parameters can respectively perform corresponding quantization on the sub-geometric prediction residual information in the three dimensions.

[0145] In this embodiment, the geometric quantization parameter includes three sub-geometric quantization parameters respectively corresponding to the sub-geometric prediction residual information in the three dimensions, which can respectively perform quantization on the sub-geometric prediction residual information in the three dimensions, so as to improve the robustness and adaptability of lossy quantization within the geometric information loop.

[0146] Optionally, the method further includes:

[0147] Performing predictive coding on the attribute information of the point cloud to be encoded to obtain attribute prediction residual information;

[0148] Quantizing the attribute prediction residual information according to the attribute quantization parameter to obtain quantized attribute prediction residual information;

[0149] Performing entropy coding based on the quantized attribute prediction residual information to obtain an attribute bitstream.

[0150] Among them, the first attribute quantization step can be determined according to the attribute quantization parameter; the attribute prediction residual information is quantized based on the first attribute quantization step and the preset attribute offset value.

[0151] Among them, the first attribute quantization step QS2 can be:

[0152]

[0153] Among them, QP2 can represent the attribute quantization parameter.

[0154] The quantized attribute prediction residual information QtRes2 obtained by quantization processing can be:

[0155]

[0156] Among them, Res2 can represent the attribute prediction residual information, and offset2 can represent the preset attribute offset value. For example, offset2 can be set to 0.5.

[0157] In addition, when performing prediction coding, a prediction candidate list can be established for the attribute information of the point cloud to be coded, the best attribute prediction value is selected from the prediction candidate list, and the difference between the best attribute prediction value and the attribute information is used to obtain the attribute prediction residual information. Each attribute prediction value in the prediction candidate list can correspond to an attribute prediction mode. For example, a prediction candidate list can be established in advance. The prediction candidate list can include N attribute prediction values, where the N attribute prediction values correspond one-to-one to N attribute prediction modes, and N is a positive integer greater than 1. Exemplarily, if the number of N is 4, that is, the prediction candidate list includes 4 attribute prediction values, and the point cloud to be coded is the 5th point cloud to be coded among all point clouds, then the attribute information of the 4 point clouds to be coded with coding orders 1 to 4 before the point cloud to be coded can be used to determine the attribute prediction values. For example, the rule for determining the attribute prediction values can be that the first attribute prediction value is the sum of the attribute information of the 4 point clouds to be coded; the second attribute prediction value is the minimum attribute information of the 4 point clouds to be coded; the third attribute prediction value is the average value of the attribute information of the 4 point clouds to be coded; the fourth attribute prediction value is the difference between the attribute information of the 4th point cloud to be coded and the attribute information of the 3rd point cloud to be coded. Among them, the attribute information of the point cloud to be coded can be characterized by the three-dimensional coordinates (x, y, z) of the point cloud to be coded.

[0158] It should be understood that the specific rule for determining the attribute prediction value can be set flexibly, and this embodiment does not make specific limitations here.

[0159] In this embodiment, prediction coding is performed on the attribute information of the point cloud to be coded to obtain attribute prediction residual information; quantization processing is performed on the attribute prediction residual information according to the attribute quantization parameter to obtain quantized attribute prediction residual information; entropy coding is performed based on the quantized attribute prediction residual information to obtain an attribute bitstream. In this way, introducing quantization processing in the process of attribute information coding can improve the coding efficiency of attribute information.

[0160] Optionally, the entropy coding based on the quantized attribute prediction residual information includes:

[0161] Determine at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information;

[0162] Obtain the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information;

[0163] Determine the target quantization attribute prediction residual information according to the rate distortion cost corresponding to the at least two candidate attribute prediction residual information;

[0164] Perform entropy coding based on the target quantization attribute prediction residual information.

[0165] Among them, the at least two candidate attribute prediction residual information may include candidate attribute prediction residual information related to the quantization attribute prediction residual information and candidate attribute prediction residual information unrelated to the quantization attribute prediction residual information. Exemplarily, when encoding colors, the at least two candidate attribute prediction residual information may include quantization attribute prediction residual information and a fixed value {0, 0, 0}.

[0166] In this embodiment, for the quantization attribute prediction residual information, a rate distortion optimization algorithm is introduced to process and obtain the target quantization attribute prediction residual information, and entropy coding is performed based on the target quantization attribute prediction residual information, which can improve the efficiency of lossy coding of attribute information.

[0167] Optionally, the target quantization attribute prediction residual information is the candidate attribute prediction residual information with the smallest rate distortion cost among the at least two candidate attribute prediction residual information.

[0168] Among them, the at least two candidate attribute prediction residual information can be stored in the form of a candidate list, and the first candidate attribute prediction residual information in the candidate list is used as the best candidate attribute prediction residual information; traverse the candidate attribute prediction residual information in the candidate list; if the rate distortion cost corresponding to the current candidate attribute prediction residual information is less than the rate distortion cost corresponding to the best candidate attribute prediction residual information, then update the current candidate attribute prediction residual information to the best candidate attribute prediction residual information, otherwise, do not update the best candidate attribute prediction residual information; after traversing the candidate list, determine the best candidate attribute prediction residual information as the target quantization attribute prediction residual information. After determining the target quantization attribute prediction residual information, the target quantization attribute prediction residual information can be input into an encoder for entropy coding.

[0169] In this embodiment, the candidate attribute prediction residual information with the smallest rate distortion cost among the at least two candidate attribute prediction residual information is determined as the target quantization attribute prediction residual information, so as to optimize the lossy coding process of attribute information and improve the point cloud coding efficiency.

[0170] Optionally, the rate distortion cost corresponding to the candidate attribute prediction residual information is determined based on an attribute distortion value and a second prediction residual bit rate, where the attribute distortion value is used to characterize the attribute distortion corresponding to the candidate attribute prediction residual information, and the second prediction residual bit rate is used to characterize the expected bit value for encoding the candidate attribute prediction residual information.

[0171] Among them, the rate-distortion cost corresponding to the candidate attribute prediction residual information may be positively correlated with both the attribute distortion value and the second prediction residual bit rate. Exemplarily, the rate-distortion cost cost2 corresponding to the candidate attribute prediction residual information may be:

[0172] cost2 = dist2 + λ2 * rate2

[0173] Among them, λ2 may represent the weight parameter of the bit rate and the distortion in the rate-distortion cost. Exemplarily, λ2 may be set to 0.4, 0.5, or 0.6, etc.; rate2 may represent the second prediction residual bit rate; dist2 may represent the attribute distortion value. The calculation formula of the attribute distortion value dist2 may be as follows:

[0174] dist2 = normal1(recAttri - oriAttri)

[0175] Among them, the function normal1 represents obtaining the first norm of the expression, recAttri represents the reconstructed attribute value obtained by using the candidate attribute prediction residual information and the attribute prediction value, and oriAttri represents the original attribute value.

[0176] In this embodiment, the rate-distortion cost corresponding to the candidate attribute prediction residual information is determined based on the attribute distortion value and the second prediction residual bit rate, and can more accurately determine the rate-distortion cost corresponding to the candidate attribute prediction residual information.

[0177] Optionally, among the at least two candidate attribute prediction residual information, there is candidate attribute prediction residual information related to the quantized attribute prediction residual information, and candidate attribute prediction residual information unrelated to the quantized attribute prediction residual information;

[0178] The entropy coding based on the target quantized attribute prediction residual information includes:

[0179] When the target quantized attribute prediction residual information is candidate attribute prediction residual information related to the quantized attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized attribute prediction residual information;

[0180] When the target quantized attribute prediction residual information is candidate attribute prediction residual information unrelated to the quantized attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized attribute prediction residual information.

[0181] Among them, the candidate attribute prediction residual information is related to the quantized attribute prediction residual information. It can be that the candidate attribute prediction residual information can be obtained based on the quantized attribute prediction residual information. For example, the candidate attribute prediction residual information is equal to the quantized attribute prediction residual information, or the candidate attribute prediction residual information is an integer multiple of the quantized attribute prediction residual information, etc.; the candidate attribute prediction residual information is not related to the quantized attribute prediction residual information. It can be that the candidate attribute prediction residual information is preset attribute prediction residual information. For example, it can be (0, 0, 0).

[0182] In addition, an attribute rate-distortion optimization control parameter can be set. If the attribute rate-distortion optimization control parameter is a third preset value, then the entropy coding based on the target quantized attribute prediction residual information includes: when the target quantized attribute prediction residual information is candidate attribute prediction residual information related to the quantized attribute prediction residual information, performing entropy coding based on the identifier corresponding to the target quantized attribute prediction residual information and the target quantized attribute prediction residual information; when the target quantized attribute prediction residual information is candidate attribute prediction residual information not related to the quantized attribute prediction residual information, performing entropy coding based on the identifier corresponding to the target quantized attribute prediction residual information.

[0183] If the attribute rate-distortion optimization control parameter is a fourth preset value, then the entropy coding based on the target quantized attribute prediction residual information includes: when the target quantized attribute prediction residual information is candidate attribute prediction residual information related to the quantized attribute prediction residual information, performing entropy coding based on the target quantized attribute prediction residual information; when the target quantized attribute prediction residual information is candidate attribute prediction residual information not related to the quantized attribute prediction residual information, performing entropy coding based on the target quantized attribute prediction residual information.

[0184] In this embodiment, the third preset value and the fourth preset value are not limited. For example, the third preset value can be 1, and the fourth preset value can be 0.

[0185] Further, it is possible to determine whether the target quantization attribute prediction residual information is related or unrelated to the quantization attribute prediction residual information by means of the identifier corresponding to the target quantization attribute prediction residual information. When decoding at the decoding end, the identifier corresponding to the target quantization attribute prediction residual information may be parsed first. If it is determined according to the identifier corresponding to the target quantization attribute prediction residual information that the target quantization attribute prediction residual information is unrelated to the quantization attribute prediction residual information, the target quantization attribute prediction residual information may be found according to the identifier corresponding to the target quantization attribute prediction residual information; if it is determined according to the identifier corresponding to the target quantization attribute prediction residual information that the target quantization attribute prediction residual information is related to the quantization attribute prediction residual information, the target quantization attribute prediction residual information may be decoded from the attribute bitstream.

[0186] In this embodiment, when the target quantization attribute prediction residual information is candidate attribute prediction residual information related to the quantization attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantization attribute prediction residual information and the target quantization attribute prediction residual information; when the target quantization attribute prediction residual information is candidate attribute prediction residual information unrelated to the quantization attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantization attribute prediction residual information. In this way, for some target quantization attribute prediction residual information, it is not necessary to encode it, but only the identifier corresponding to the target quantization attribute prediction residual information is encoded, which can further improve the coding efficiency.

[0187] See the figure Figure 6 is a flowchart of a point cloud decoding processing method provided by an embodiment of the present application. As Figure 6 shown, the point cloud decoding processing method includes the following steps:

[0188] Step 201, perform entropy decoding on the geometry bitstream to obtain quantization geometry prediction residual information;

[0189] Step 202, perform inverse quantization processing on the quantization geometry prediction residual information according to the geometry quantization parameter to obtain geometry prediction residual information;

[0190] Step 203, perform prediction decoding based on the geometry prediction residual information to obtain the geometry information of the point cloud to be decoded.

[0191] Among them, the geometric bitstream can be entropy decoded to obtain quantized geometric prediction residual information and geometric prediction modes. Prediction decoding can be performed based on the geometric prediction residual information and geometric prediction modes to obtain the geometric information of the point cloud to be decoded. Exemplarily, the geometric prediction modes can be parsed, and corresponding geometric prediction values can be selected according to the geometric prediction modes; the geometric prediction values are added to the geometric prediction residual information to obtain the geometric information of the point cloud to be decoded. The geometric information can include geometric coordinates.

[0192] Optionally, the entropy decoding of the geometric bitstream to obtain quantized geometric prediction residual information includes:

[0193] Determine whether the geometric quantization control parameter indicates enabling quantization processing;

[0194] In the case where the geometric quantization control parameter indicates enabling quantization processing, entropy decode the geometric bitstream to obtain quantized geometric prediction residual information.

[0195] Optionally, after determining whether the geometric quantization control parameter indicates enabling quantization processing, the method further includes:

[0196] In the case where the geometric quantization control parameter indicates not enabling quantization processing, entropy decode the geometric bitstream to obtain geometric prediction residual information.

[0197] Optionally, the inverse quantization processing of the quantized geometric prediction residual information according to the geometric quantization parameter includes:

[0198] Determine a second geometric quantization step size according to the geometric quantization parameter;

[0199] Perform inverse quantization processing on the quantized geometric prediction residual information based on the second geometric quantization step size and a second preset geometric offset value.

[0200] Among them, the second geometric quantization step size QS3 can be:

[0201]

[0202] Among them, 2 shift3 can represent the second preset geometric offset value, shift3 can represent the number of bits shifted during the quantization process, the larger shift3 is, the more accurate the quantization result is, and QP1 can represent the geometric quantization parameter.

[0203] Exemplarily, shift3 can be configured to 6.

[0204] The geometric prediction residual information RQtRes1 obtained by performing inverse quantization processing can be:

[0205]

[0206] Among them, QtRes1 can represent the quantized geometric prediction residual information, and offset3 can represent half of the second preset geometric offset value, that is, offset3 is 2 shift3-1 .

[0207] Optionally, the geometric prediction residual information includes sub-geometric prediction residual information in three dimensions;

[0208] The geometric quantization parameters include three sub-geometric quantization parameters corresponding to the sub-geometric prediction residual information in the three dimensions respectively.

[0209] Optionally, the method further includes:

[0210] Performing entropy decoding on the attribute bitstream to obtain quantized attribute prediction residual information;

[0211] Performing inverse quantization processing on the quantized attribute prediction residual information according to the attribute quantization parameters to obtain attribute prediction residual information;

[0212] Performing prediction decoding based on the attribute prediction residual information to obtain the attribute information of the point cloud to be decoded.

[0213] Among them, performing entropy decoding on the attribute bitstream to obtain quantized attribute prediction residual information may include: determining whether the attribute quantization control parameter indicates enabling quantization processing; when the attribute quantization control parameter indicates enabling quantization processing, performing entropy decoding on the attribute bitstream to obtain quantized attribute prediction residual information; when the attribute quantization control parameter indicates not enabling quantization processing, performing entropy decoding on the attribute bitstream to obtain attribute prediction residual information.

[0214] Among them, entropy decoding can be performed on the attribute bitstream to obtain quantized attribute prediction residual information and an attribute prediction mode. Prediction decoding can be performed based on the attribute prediction residual information and the attribute prediction mode to obtain the attribute information of the point cloud to be decoded. Exemplarily, the attribute prediction mode can be parsed, and a corresponding attribute prediction value can be selected according to the attribute prediction mode; the attribute prediction value is added to the attribute prediction residual information to obtain the attribute information of the point cloud to be decoded. The attribute information may include attribute coordinates.

[0215] Among them, a second attribute quantization step size can be determined according to the attribute quantization parameters, and the second attribute quantization step size QS4 can be:

[0216]

[0217] Among them, QP2 can represent the attribute quantization parameter.

[0218] The attribute prediction residual information RQtRes2 obtained by performing inverse quantization processing can be:

[0219] RQtRes2 = QtRes2 · QS4

[0220] Wherein, QtRes2 can represent the quantized attribute prediction residual information.

[0221] It should be noted that, as the implementation manner on the decoding side corresponding to the Figure 3 embodiment shown, the specific implementation manner can refer to the Figure 3 relevant description of the embodiment shown. To avoid repeated description, this embodiment will not be elaborated herein, and the same beneficial effects can still be achieved.

[0222] It should be noted that for the point cloud encoding processing method provided in the embodiments of the present application, the execution subject can be a point cloud encoding processing device, or a control module in the point cloud encoding processing device for executing the method of point cloud encoding processing. In the embodiments of the present application, taking the point cloud encoding processing device executing the method of point cloud encoding processing as an example, the point cloud encoding processing device provided in the embodiments of the present application is described.

[0223] Please refer to Figure 7 , Figure 7 which is one of the structural diagrams of a point cloud encoding processing device provided in the embodiments of the present application. As shown in Figure 7 , the point cloud encoding processing device 300 includes:

[0224] A first encoding module 301, configured to perform prediction encoding based on the geometric information of the point cloud to be encoded, and obtain geometric prediction residual information;

[0225] A first quantization module 302, configured to perform quantization processing on the geometric prediction residual information according to geometric quantization parameters, and obtain quantized geometric prediction residual information;

[0226] A second encoding module 303, configured to perform entropy encoding based on the quantized geometric prediction residual information, and obtain a geometric bitstream.

[0227] Optionally, the first quantization module 302 is specifically configured to:

[0228] Determine whether the geometric quantization control parameter indicates to enable quantization processing;

[0229] In the case where the geometric quantization control parameter indicates to enable quantization processing, perform quantization processing on the geometric prediction residual information according to geometric quantization parameters.

[0230] Optionally, the first quantization module 302 is further specifically configured to:

[0231] In the case where the geometric quantization control parameter indicates not to enable quantization processing, perform entropy encoding based on the geometric prediction residual information, and obtain a geometric bitstream.

[0232] Optionally, the first encoding module 301 is specifically configured to:

[0233] Divide the point cloud to be encoded into a first sub-point cloud to be encoded and a second sub-point cloud to be encoded based on the node identifier corresponding to the point cloud to be encoded;

[0234] When the geometric encoding control parameter indicates the first encoding mode, perform predictive encoding on the geometric information of the first sub-point cloud to be encoded;

[0235] When the geometric encoding control parameter indicates the second encoding mode, perform predictive encoding on the geometric information of the second sub-point cloud to be encoded.

[0236] Optionally, as Figure 8 shown, the second encoding module 303 specifically includes:

[0237] A first determination unit 3031, configured to determine at least two candidate geometric prediction residual information based on the quantized geometric prediction residual information;

[0238] A first acquisition unit 3032, configured to acquire the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information;

[0239] A second determination unit 3033, configured to determine the target quantized geometric prediction residual information according to the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information;

[0240] A first encoding unit 3034, configured to perform entropy encoding based on the target quantized geometric prediction residual information.

[0241] Optionally, the target quantized geometric prediction residual information is the candidate geometric prediction residual information with the smallest rate-distortion cost among the at least two candidate geometric prediction residual information.

[0242] Optionally, the rate-distortion cost corresponding to the candidate geometric prediction residual information is determined based on the geometric distortion value and the first prediction residual bit rate, where the geometric distortion value is used to characterize the geometric distortion corresponding to the candidate geometric prediction residual information, and the first prediction residual bit rate is used to characterize the expected number of bits for encoding the candidate geometric prediction residual information.

[0243] Optionally, among the at least two candidate geometric prediction residual information, there is candidate geometric prediction residual information related to the quantized geometric prediction residual information and candidate geometric prediction residual information not related to the quantized geometric prediction residual information;

[0244] The first encoding unit 3034 is specifically configured to:

[0245] When the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information;

[0246] When the target quantized geometric prediction residual information is candidate geometric prediction residual information not related to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information.

[0247] Optionally, the first coding module 301 is specifically configured to:

[0248] Obtain the quantized point cloud corresponding to the point cloud to be encoded according to a preset first quantization step size;

[0249] Perform duplicate removal processing on the quantized point cloud;

[0250] Perform predictive coding on the geometric information of the point cloud to be encoded corresponding to the quantized point cloud obtained after duplicate removal processing.

[0251] Optionally, the first quantization module 302 is specifically configured to:

[0252] Determine a first geometric quantization step size according to geometric quantization parameters;

[0253] Quantize the geometric prediction residual information based on the first geometric quantization step size and a first preset geometric offset value.

[0254] Optionally, the geometric prediction residual information includes sub-geometric prediction residual information in three dimensions;

[0255] The geometric quantization parameters include three sub-geometric quantization parameters corresponding to the sub-geometric prediction residual information in the three dimensions respectively.

[0256] Optionally, as Figure 9 shown, the apparatus 300 further includes:

[0257] A third coding module 304, configured to perform predictive coding on the attribute information of the point cloud to be encoded to obtain attribute prediction residual information;

[0258] A second quantization module 305, configured to quantize the attribute prediction residual information according to attribute quantization parameters to obtain quantized attribute prediction residual information;

[0259] A fourth coding module 306, configured to perform entropy coding based on the quantized attribute prediction residual information to obtain an attribute bitstream.

[0260] Optionally, as Figure 10As shown, the fourth encoding module 306 specifically includes:

[0261] A third determination unit 3061, configured to determine at least two candidate attribute prediction residual information based on the quantization attribute prediction residual information;

[0262] A second acquisition unit 3062, configured to acquire the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information;

[0263] A fourth determination unit 3063, configured to determine target quantization attribute prediction residual information according to the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information;

[0264] A second encoding unit 3064, configured to perform entropy encoding based on the target quantization attribute prediction residual information.

[0265] Optionally, the target quantization attribute prediction residual information is the candidate attribute prediction residual information with the smallest rate-distortion cost among the at least two candidate attribute prediction residual information.

[0266] Optionally, the rate-distortion cost corresponding to the candidate attribute prediction residual information is determined based on an attribute distortion value and a second prediction residual bit rate, where the attribute distortion value is used to characterize the attribute distortion corresponding to the candidate attribute prediction residual information, and the second prediction residual bit rate is used to characterize the expected bit value for encoding the candidate attribute prediction residual information.

[0267] Optionally, among the at least two candidate attribute prediction residual information, there is candidate attribute prediction residual information related to the quantization attribute prediction residual information and candidate attribute prediction residual information unrelated to the quantization attribute prediction residual information;

[0268] The second encoding unit 3064 is specifically configured to:

[0269] When the target quantization attribute prediction residual information is the candidate attribute prediction residual information related to the quantization attribute prediction residual information, perform entropy encoding based on the identifier corresponding to the target quantization geometric prediction residual information and the target quantization attribute prediction residual information;

[0270] When the target quantization attribute prediction residual information is the candidate attribute prediction residual information unrelated to the quantization attribute prediction residual information, perform entropy encoding based on the identifier corresponding to the target quantization attribute prediction residual information.

[0271] The point cloud encoding processing device 300 in the embodiments of the present application can improve the rate control effect of the geometric bitstream of the point cloud.

[0272] The point cloud encoding processing device in the embodiments of the present application may be a device, a device with an operating system, or an electronic device, or may be a component, an integrated circuit, or a chip in a terminal. The device or electronic device may be a mobile terminal or a non-mobile terminal. Exemplarily, the mobile terminal may include, but is not limited to, the types of terminals listed above, and the non-mobile terminal may be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., which are not specifically limited in the embodiments of the present application.

[0273] The point cloud encoding processing device provided in the embodiments of the present application can implement Figure 3 each process implemented by the method embodiments and achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0274] It should be noted that for the point cloud decoding processing method provided in the embodiments of the present application, the execution subject may be a point cloud decoding processing device, or a control module in the point cloud decoding processing device for executing the method of point cloud decoding processing. In the embodiments of the present application, the method of point cloud decoding processing executed by the point cloud decoding processing device is taken as an example to illustrate the point cloud decoding processing device provided in the embodiments of the present application.

[0275] Please refer to Figure 11 , Figure 11 which is one of the structural diagrams of a point cloud decoding processing device provided in the embodiments of the present application. As Figure 11 shown, the point cloud decoding processing device 400 includes:

[0276] A first decoding module 401, configured to perform entropy decoding on the geometry bitstream to obtain quantized geometry prediction residual information;

[0277] A first dequantization module 402, configured to perform dequantization processing on the quantized geometry prediction residual information according to the geometry quantization parameter to obtain geometry prediction residual information;

[0278] A second decoding module 403, configured to perform prediction decoding based on the geometry prediction residual information to obtain the geometry information of the point cloud to be decoded.

[0279] Optionally, the first decoding module 401 is specifically configured to:

[0280] Determine whether the geometry quantization control parameter indicates to enable quantization processing;

[0281] In the case where the geometry quantization control parameter indicates to enable quantization processing, perform entropy decoding on the geometry bitstream to obtain quantized geometry prediction residual information.

[0282] Optionally, the first decoding module 401 is further specifically configured to:

[0283] When the geometric quantization control parameter indicates that quantization processing is not enabled, perform entropy decoding on the geometric bitstream to obtain geometric prediction residual information.

[0284] Optionally, the first dequantization module 402 is specifically configured to:

[0285] Determine a second geometric quantization step size according to geometric quantization parameters;

[0286] Perform dequantization processing on the quantized geometric prediction residual information based on the second geometric quantization step size and a second preset geometric offset value.

[0287] Optionally, the geometric prediction residual information includes sub-geometric prediction residual information in three dimensions;

[0288] The geometric quantization parameters include three sub-geometric quantization parameters respectively corresponding to the sub-geometric prediction residual information in the three dimensions.

[0289] Optionally, as Figure 12 shown, the apparatus 400 further includes:

[0290] A third decoding module 404, configured to perform entropy decoding on an attribute bitstream to obtain quantized attribute prediction residual information;

[0291] A second dequantization module 405, configured to perform dequantization processing on the quantized attribute prediction residual information according to attribute quantization parameters to obtain attribute prediction residual information;

[0292] A fourth decoding module 406, configured to perform prediction decoding based on the attribute prediction residual information to obtain attribute information of the to-be-decoded point cloud.

[0293] The point cloud decoding processing apparatus 400 in the embodiments of the present application can improve the rate control effect of the geometric bitstream of the point cloud.

[0294] The point cloud decoding processing apparatus in the embodiments of the present application may be a device, a device with an operating system or an electronic device, or may also be a component, an integrated circuit, or a chip in a terminal. The device or electronic device may be a mobile terminal or a non-mobile terminal. Exemplarily, the mobile terminal may include, but is not limited to, the types of terminals listed above, and the non-mobile terminal may be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., which are not specifically limited in the embodiments of the present application.

[0295] The point cloud decoding processing device provided by the embodiments of the present application can implement Figure 6 each process implemented by the method embodiments described above, and achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0296] Optionally, as Figure 13 shown, the embodiments of the present application further provide a communication device 500, including a processor 501, a memory 502, a program or instruction stored on the memory 502 and executable on the processor 501. For example, when the communication device 500 is a terminal, when the program or instruction is executed by the processor 501, it implements each process of the above-mentioned point cloud encoding processing method embodiment, and can achieve the same technical effects; or, when the program or instruction is executed by the processor 501, it implements each process of the above-mentioned point cloud decoding processing method embodiment, and can achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0297] The embodiments of the present application further provide a terminal, including a processor and a communication interface. This terminal embodiment corresponds to the above-mentioned point cloud encoding processing method embodiment, or this terminal embodiment corresponds to the above-mentioned point cloud decoding processing method embodiment. Each implementation process and implementation manner of the above method embodiments can be applied to this terminal embodiment, and can achieve the same technical effects. Specifically, Figure 14 FIG. is a schematic hardware structure diagram of a terminal for implementing an embodiment of the present application.

[0298] The terminal 600 includes, but is not limited to: at least some components such as a radio frequency unit 601, a network module 602, an audio output unit 603, an input unit 604, a sensor 605, a display unit 606, a user input unit 607, an interface unit 608, a memory 609, and a processor 610.

[0299] Those skilled in the art can understand that the terminal 600 may further include a power supply (such as a battery) for supplying power to each component. The power supply can be logically connected to the processor 610 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 6 The terminal structure shown in FIG. does not limit the terminal. The terminal may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0300] It should be understood that in the embodiments of the present application, the input unit 604 may include a Graphics Processing Unit (GPU) 6041 and a microphone 6042. The GPU 6041 processes the image data of the static pictures or videos obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode. The display unit 606 may include a display panel 6061, and the display panel 6061 may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 607 includes a touch panel 6071 and other input devices 6072. The touch panel 6071 is also called a touch screen. The touch panel 6071 may include two parts: a touch detection device and a touch controller. The other input devices 6072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be elaborated here.

[0301] In the embodiments of the present application, after receiving the downlink data from the network-side device, the radio frequency unit 601 sends it to the processor 610 for processing; in addition, it sends the uplink data to the network-side device. Generally, the radio frequency unit 601 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0302] The memory 609 can be used to store software programs or instructions and various data. The memory 609 mainly includes a program or instruction storage area and a data storage area. Among them, the program or instruction storage area can store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 609 may include a high-speed random access memory and may also include a non-volatile memory. The non-volatile memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. For example, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0303] The processor 610 may include one or more processing units; optionally, the processor 610 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, the user interface, and applications or instructions, etc., and the modem processor mainly processes wireless communications, such as a baseband processor. It can be understood that the above-mentioned modem processor may not be integrated into the processor 610 either.

[0304] Wherein, when the terminal is used to execute the point cloud encoding processing method:

[0305] The processor or the communication interface is used to: perform predictive encoding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information; perform quantization processing on the geometric prediction residual information according to the geometric quantization parameter to obtain quantized geometric prediction residual information; perform entropy encoding based on the quantized geometric prediction residual information to obtain a geometric bitstream.

[0306] Optionally, the processor 610 is further used to:

[0307] Determine whether the geometric quantization control parameter indicates to enable quantization processing;

[0308] When the geometric quantization control parameter indicates to enable quantization processing, perform quantization processing on the geometric prediction residual information according to the geometric quantization parameter.

[0309] Optionally, the processor 610 is further used to:

[0310] When the geometric quantization control parameter indicates not to enable quantization processing, perform entropy encoding based on the geometric prediction residual information to obtain a geometric bitstream.

[0311] Optionally, the processor 610 is further used to:

[0312] Divide the point cloud to be encoded into a first sub-point cloud to be encoded and a second sub-point cloud based on the node identifier corresponding to the point cloud to be encoded;

[0313] When the geometric encoding control parameter indicates the first encoding mode, perform predictive encoding on the geometric information of the first sub-point cloud to be encoded;

[0314] When the geometric encoding control parameter indicates the second encoding mode, perform predictive encoding on the geometric information of the second sub-point cloud to be encoded.

[0315] Optionally, the processor 610 is further used to:

[0316] Determine at least two candidate geometric prediction residual information based on the quantized geometric prediction residual information;

[0317] Obtain the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information;

[0318] Determine the target quantized geometric prediction residual information according to the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information;

[0319] Perform entropy encoding based on the target quantized geometric prediction residual information.

[0320] Optionally, the target quantized geometric prediction residual information is the candidate geometric prediction residual information with the minimum rate-distortion cost among the at least two candidate geometric prediction residual information.

[0321] Optionally, the rate-distortion cost corresponding to the candidate geometric prediction residual information is determined based on a geometric distortion value and a first prediction residual bit rate, where the geometric distortion value is used to characterize the geometric distortion corresponding to the candidate geometric prediction residual information, and the first prediction residual bit rate is used to characterize the expected bit value for encoding the candidate geometric prediction residual information.

[0322] Optionally, among the at least two candidate geometric prediction residual information, there is candidate geometric prediction residual information related to the quantized geometric prediction residual information and candidate geometric prediction residual information unrelated to the quantized geometric prediction residual information;

[0323] The processor 610 is further configured to:

[0324] When the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, perform entropy coding based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information;

[0325] When the target quantized geometric prediction residual information is candidate geometric prediction residual information unrelated to the quantized geometric prediction residual information, perform entropy coding based on the identifier corresponding to the target quantized geometric prediction residual information.

[0326] Optionally, the processor 610 is further configured to:

[0327] Obtain a quantized point cloud corresponding to the point cloud to be encoded according to a preset first quantization step;

[0328] Perform a duplicate removal process on the quantized point cloud;

[0329] Perform predictive coding on the geometric information of the point cloud to be encoded corresponding to the quantized point cloud obtained after the duplicate removal process.

[0330] Optionally, the processor 610 is further configured to:

[0331] Determine a first geometric quantization step according to geometric quantization parameters;

[0332] Perform quantization processing on the geometric prediction residual information based on the first geometric quantization step and a first preset geometric offset value.

[0333] Optionally, the geometric prediction residual information includes sub-geometric prediction residual information in three dimensions;

[0334] The geometric quantization parameters include three sub-geometric quantization parameters corresponding to the sub-geometric prediction residual information of the three dimensions respectively.

[0335] Optionally, the processor 610 is further configured to:

[0336] Perform predictive coding on the attribute information of the point cloud to be encoded to obtain attribute prediction residual information;

[0337] Quantize the attribute prediction residual information according to the attribute quantization parameter to obtain quantized attribute prediction residual information;

[0338] Perform entropy coding based on the quantized attribute prediction residual information to obtain an attribute bitstream.

[0339] Optionally, the processor 610 is further configured to:

[0340] Determine at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information;

[0341] Obtain the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information;

[0342] Determine the target quantized attribute prediction residual information according to the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information;

[0343] Perform entropy coding based on the target quantized attribute prediction residual information.

[0344] Optionally, the target quantized attribute prediction residual information is the candidate attribute prediction residual information with the minimum rate-distortion cost among the at least two candidate attribute prediction residual information.

[0345] Optionally, the rate-distortion cost corresponding to the candidate attribute prediction residual information is determined based on the attribute distortion value and the second prediction residual bit rate, where the attribute distortion value is used to characterize the attribute distortion corresponding to the candidate attribute prediction residual information, and the second prediction residual bit rate is used to characterize the expected bit value for encoding the candidate attribute prediction residual information.

[0346] Optionally, candidate attribute prediction residual information related to, and candidate attribute prediction residual information unrelated to the quantized attribute prediction residual information;

[0347] The processor 610 is further configured to:

[0348] In the case where the target quantized attribute prediction residual information is the candidate attribute prediction residual information related to the quantized attribute prediction residual information, perform entropy coding based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized attribute prediction residual information;

[0349] When the target quantization attribute prediction residual information is candidate attribute prediction residual information that is not related to the quantization attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantization attribute prediction residual information.

[0350] The terminal in the embodiments of the present application can improve the rate control effect of the geometric bitstream of the point cloud.

[0351] Specifically, the terminal in the embodiments of the present application further includes: instructions or programs stored on the memory 609 and executable on the processor 610. The processor 610 calls the instructions or programs in the memory 609 to execute Figure 7 the methods executed by the modules shown, and achieves the same technical effects. To avoid repetition, it will not be elaborated here.

[0352] Wherein, when the terminal is used to execute the point cloud decoding processing method:

[0353] The processor or the communication interface is used to: perform entropy decoding on the geometric bitstream to obtain quantization geometric prediction residual information; perform inverse quantization processing on the quantization geometric prediction residual information according to the geometric quantization parameter to obtain geometric prediction residual information; perform prediction decoding based on the geometric prediction residual information to obtain the geometric information of the point cloud to be decoded.

[0354] Optionally, the processor 610 is further used to:

[0355] Determine whether the geometric quantization control parameter indicates to enable quantization processing;

[0356] When the geometric quantization control parameter indicates to enable quantization processing, perform entropy decoding on the geometric bitstream to obtain quantization geometric prediction residual information.

[0357] Optionally, the processor 610 is further used to:

[0358] When the geometric quantization control parameter indicates not to enable quantization processing, perform entropy decoding on the geometric bitstream to obtain geometric prediction residual information.

[0359] Optionally, the processor 610 is further used to:

[0360] Determine the second geometric quantization step according to the geometric quantization parameter;

[0361] Perform inverse quantization processing on the quantization geometric prediction residual information based on the second geometric quantization step and the second preset geometric offset value.

[0362] Optionally, the geometric prediction residual information includes sub-geometric prediction residual information in three dimensions;

[0363] The geometric quantization parameters include three sub-geometric quantization parameters respectively corresponding to the sub-geometric prediction residual information of the three dimensions.

[0364] Optionally, the processor 610 is further configured to:

[0365] perform entropy decoding on the attribute bitstream to obtain quantized attribute prediction residual information;

[0366] perform inverse quantization processing on the quantized attribute prediction residual information according to the attribute quantization parameters to obtain attribute prediction residual information;

[0367] perform prediction decoding based on the attribute prediction residual information to obtain the attribute information of the to-be-decoded point cloud.

[0368] The terminal in the embodiments of the present application can improve the rate control effect of the geometric bitstream of the point cloud.

[0369] Specifically, the terminal in the embodiments of the present application further includes: instructions or programs stored on the memory 609 and executable on the processor 610. The processor 610 calls the instructions or programs in the memory 609 to execute Figure 11 the methods executed by the modules shown, and achieves the same technical effects. To avoid repetition, they are not described herein again.

[0370] The embodiments of the present application further provide a readable storage medium. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the various processes of the above-mentioned point cloud encoding processing method embodiments are implemented, or when the programs or instructions are executed by a processor, the various processes of the above-mentioned point cloud decoding processing method embodiments are implemented, and the same technical effects can be achieved. To avoid repetition, they are not described herein again.

[0371] Wherein, the processor is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0372] The embodiments of the present application further provide a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-mentioned point cloud encoding processing method embodiments, or to implement the various processes of the above-mentioned point cloud decoding processing method embodiments, and the same technical effects can be achieved. To avoid repetition, they are not described herein again.

[0373] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-chip, etc.

[0374] It should be noted that, in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising such element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0375] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0376] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the purpose of the present application and the scope protected by the claims, can still make many forms, all of which fall within the protection scope of the present application.

Claims

1. A point cloud encoding processing method, characterized in that, Including: Performing predictive coding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information; Quantizing the geometric prediction residual information according to geometric quantization parameters to obtain quantized geometric prediction residual information; Performing entropy coding based on the quantized geometric prediction residual information to obtain a geometric bitstream.

2. The method according to claim 1, wherein The quantizing the geometric prediction residual information according to geometric quantization parameters includes: Determining whether the geometric quantization control parameter indicates enabling quantization processing; When the geometric quantization control parameter indicates enabling quantization processing, quantizing the geometric prediction residual information according to geometric quantization parameters.

3. The method according to claim 2, wherein After determining whether the geometric quantization control parameter indicates enabling quantization processing, the method further includes: When the geometric quantization control parameter indicates not enabling quantization processing, performing entropy coding based on the geometric prediction residual information to obtain a geometric bitstream.

4. The method according to claim 1, characterized in that, The performing predictive coding based on the geometric information of the point cloud to be encoded includes: Dividing the point cloud to be encoded into a first sub-point cloud to be encoded and a second sub-point cloud to be encoded based on the node identifier corresponding to the point cloud to be encoded; When the geometric coding control parameter indicates a first coding mode, performing predictive coding on the geometric information of the first sub-point cloud to be encoded; When the geometric coding control parameter indicates a second coding mode, performing predictive coding on the geometric information of the second sub-point cloud to be encoded.

5. The method according to claim 1, characterized in that The performing entropy coding based on the quantized geometric prediction residual information includes: Determining at least two candidate geometric prediction residual information based on the quantized geometric prediction residual information; Obtaining the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information; Determining target quantized geometric prediction residual information according to the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information; Performing entropy coding based on the target quantized geometric prediction residual information.

6. The method according to claim 5, wherein The target quantized geometric prediction residual information is the candidate geometric prediction residual information with the minimum rate-distortion cost among the at least two candidate geometric prediction residual information.

7. The method according to claim 5, characterized in that, The rate-distortion cost corresponding to the candidate geometric prediction residual information is determined based on a geometric distortion value and a first prediction residual bit rate, where the geometric distortion value is used to characterize the geometric distortion corresponding to the candidate geometric prediction residual information, and the first prediction residual bit rate is used to characterize the expected bit value for encoding the candidate geometric prediction residual information.

8. The method according to claim 5, characterized in that, Among the at least two candidate geometric prediction residual information, there is candidate geometric prediction residual information related to the quantized geometric prediction residual information and candidate geometric prediction residual information not related to the quantized geometric prediction residual information; The performing entropy coding based on the target quantized geometric prediction residual information includes: When the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, performing entropy coding based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information; In the case that the target quantized geometric prediction residual information is candidate geometric prediction residual information that is not related to the quantized geometric prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information.

9. The method according to claim 1, wherein The prediction coding based on the geometric information of the point cloud to be encoded includes: Obtaining a quantized point cloud corresponding to the point cloud to be encoded according to a preset first quantization step; Performing a duplicate removal process on the quantized point cloud; Performing prediction coding on the geometric information of the point cloud to be encoded corresponding to the quantized point cloud obtained after the duplicate removal process.

10. The method according to claim 1, wherein The quantizing process of the geometric prediction residual information according to the geometric quantization parameter includes: Determining a first geometric quantization step according to the geometric quantization parameter; Quantizing the geometric prediction residual information based on the first geometric quantization step and a first preset geometric offset value.

11. The method according to claim 1, wherein The geometric prediction residual information includes sub-geometric prediction residual information in three dimensions; The geometric quantization parameter includes three sub-geometric quantization parameters corresponding to the sub-geometric prediction residual information in the three dimensions respectively.

12. The method according to claim 1, wherein The method further includes: Performing prediction coding on the attribute information of the point cloud to be encoded to obtain attribute prediction residual information; Quantizing the attribute prediction residual information according to the attribute quantization parameter to obtain quantized attribute prediction residual information; Performing entropy coding based on the quantized attribute prediction residual information to obtain an attribute bitstream.

13. The method according to claim 12, wherein The entropy coding based on the quantized attribute prediction residual information includes: Determining at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information; Obtaining the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information; Determining target quantized attribute prediction residual information according to the rate-distortion cost corresponding to the at least two candidate attribute prediction residual information; Performing entropy coding based on the target quantized attribute prediction residual information.

14. The method according to claim 13, wherein The target quantized attribute prediction residual information is the candidate attribute prediction residual information with the minimum rate-distortion cost among the at least two candidate attribute prediction residual information.

15. The method according to claim 13, wherein The rate-distortion cost corresponding to the candidate attribute prediction residual information is determined based on an attribute distortion value and a second prediction residual bit rate, where the attribute distortion value is used to characterize the attribute distortion corresponding to the candidate attribute prediction residual information, and the second prediction residual bit rate is used to characterize the expected number of bits for encoding the candidate attribute prediction residual information.

16. The method according to claim 13, wherein Among the at least two candidate attribute prediction residual information, there is candidate attribute prediction residual information related to the quantized attribute prediction residual information and candidate attribute prediction residual information not related to the quantized attribute prediction residual information; The entropy coding based on the target quantized attribute prediction residual information includes: In the case that the target quantized attribute prediction residual information is candidate attribute prediction residual information related to the quantized attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantized geometric prediction residual information and the target quantized attribute prediction residual information; In the case where the target quantization attribute prediction residual information is candidate attribute prediction residual information that is not related to the quantization attribute prediction residual information, entropy coding is performed based on the identifier corresponding to the target quantization attribute prediction residual information.

17. A method for point cloud decoding and processing, characterized in that Including: Entropy decoding the geometric bitstream to obtain quantization geometric prediction residual information; Inverse quantizing the quantization geometric prediction residual information according to the geometric quantization parameter to obtain geometric prediction residual information; Performing prediction decoding based on the geometric prediction residual information to obtain the geometric information of the point cloud to be decoded.

18. The method according to claim 17, wherein The entropy decoding the geometric bitstream to obtain quantization geometric prediction residual information includes: Determining whether the geometric quantization control parameter indicates enabling quantization processing; In the case where the geometric quantization control parameter indicates enabling quantization processing, entropy decoding the geometric bitstream to obtain quantization geometric prediction residual information.

19. The method according to claim 18, characterized in that, After determining whether the geometric quantization control parameter indicates enabling quantization processing, the method further includes: In the case where the geometric quantization control parameter indicates not enabling quantization processing, entropy decoding the geometric bitstream to obtain geometric prediction residual information.

20. The method according to claim 17, wherein The inverse quantizing the quantization geometric prediction residual information according to the geometric quantization parameter includes: Determining a second geometric quantization step according to the geometric quantization parameter; Inverse quantizing the quantization geometric prediction residual information based on the second geometric quantization step and a second preset geometric offset value.

21. The method according to claim 17, characterized in that The geometric prediction residual information includes sub-geometric prediction residual information in three dimensions; The geometric quantization parameter includes three sub-geometric quantization parameters respectively corresponding to the sub-geometric prediction residual information in the three dimensions.

22. The method according to claim 17, wherein The method further includes: Entropy decoding the attribute bitstream to obtain quantization attribute prediction residual information; Inverse quantizing the quantization attribute prediction residual information according to the attribute quantization parameter to obtain attribute prediction residual information; Performing prediction decoding based on the attribute prediction residual information to obtain the attribute information of the point cloud to be decoded.

23. A point cloud encoding processing device, characterized in that, Including: A first encoding module, configured to perform prediction encoding based on the geometric information of the point cloud to be encoded to obtain geometric prediction residual information; A first quantization module, configured to quantize the geometric prediction residual information according to the geometric quantization parameter to obtain quantization geometric prediction residual information; A second encoding module, configured to perform entropy encoding based on the quantization geometric prediction residual information to obtain a geometric bitstream.

24. The device according to claim 23, characterized in that, The first quantization module is specifically configured to: Determine whether the geometric quantization control parameter indicates enabling quantization processing; In the case where the geometric quantization control parameter indicates enabling quantization processing, quantize the geometric prediction residual information according to the geometric quantization parameter.

25. The device according to claim 23, characterized in that, The second encoding module specifically includes: A first determining unit, configured to determine at least two candidate geometric prediction residual information based on the quantization geometric prediction residual information; A first obtaining unit, configured to obtain the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information; A second determining unit, configured to determine target quantization geometric prediction residual information according to the rate-distortion cost corresponding to the at least two candidate geometric prediction residual information; A first coding unit for performing entropy coding based on the target quantized geometric prediction residual information.

26. The device according to claim 23, wherein, The apparatus further includes: A third coding module for performing predictive coding on the attribute information of the point cloud to be coded to obtain attribute prediction residual information; A second quantization module for quantizing the attribute prediction residual information according to the attribute quantization parameter to obtain quantized attribute prediction residual information; A fourth coding module for performing entropy coding based on the quantized attribute prediction residual information to obtain an attribute bitstream.

27. The device according to claim 26, wherein The fourth coding module specifically includes: A third determination unit for determining at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information; A second acquisition unit for acquiring the rate-distortion costs corresponding to the at least two candidate attribute prediction residual information; A fourth determination unit for determining the target quantized attribute prediction residual information according to the rate-distortion costs corresponding to the at least two candidate attribute prediction residual information; A second coding unit for performing entropy coding based on the target quantized attribute prediction residual information.

28. A point cloud decoding processing device, characterized in that, including: A first decoding module for performing entropy decoding on the geometric bitstream to obtain quantized geometric prediction residual information; A first inverse quantization module for performing inverse quantization processing on the quantized geometric prediction residual information according to the geometric quantization parameter to obtain geometric prediction residual information; A second decoding module for performing predictive decoding based on the geometric prediction residual information to obtain the geometric information of the point cloud to be decoded.

29. A terminal, characterized in that, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor, the program or instruction, when executed by the processor, implements the steps of the point cloud coding processing method according to any one of claims 1 to 16; or, the program or instruction, when executed by the processor, implements the steps of the point cloud decoding processing method according to any one of claims 17 to 22.

30. A readable storage medium, characterized in that, The program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, it implements the steps of the point cloud coding processing method according to any one of claims 1 to 16, or when the program or instruction is executed by the processor, it implements the steps of the point cloud decoding processing method according to any one of claims 17 to 22.

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