CN115474051A

CN115474051A - Point cloud encoding method, point cloud decoding method and terminal

Info

Publication number: CN115474051A
Application number: CN202110654078.5A
Authority: CN
Inventors: 张伟; 孙泽星; 吕卓逸; 杨付正
Original assignee: Vivo Mobile Communication Co Ltd
Current assignee: Vivo Mobile Communication Co Ltd
Priority date: 2021-06-11
Filing date: 2021-06-11
Publication date: 2022-12-13
Also published as: WO2022257968A1

Abstract

The application discloses a point cloud encoding method, a point cloud decoding method and a terminal, which belong to the technical field of point cloud processing, and the point cloud encoding method of the embodiment of the application comprises the following steps: acquiring a first identification parameter of a first target point cloud to be coded; an encoding operation is performed on the first target point cloud based on a first identification parameter. Wherein the encoding operation comprises at least one of: under the condition that the first identification parameter is used for representing parallel coding, performing geometric coding and attribute predictive coding on the first target point cloud in parallel to obtain a coding result of the first target point cloud; and performing geometric prediction coding on at least part of points to be coded of the first target point cloud. In an embodiment of the application, geometric coding and attribute prediction coding are performed on a first target point cloud in parallel based on a first identification parameter of the first target point cloud. By performing geometric predictive coding on at least part of points to be coded of the first target point cloud, the execution of multi-branch tree coding on all points to be coded of the first target point cloud is avoided.

Description

Point cloud encoding method, point cloud decoding method and terminal

Technical Field

The application belongs to the technical field of point cloud processing, and particularly relates to a point cloud encoding method, a point cloud decoding method and a terminal.

Background

The point cloud is a set of randomly distributed discrete points in space that represent the spatial structure and surface attributes of a three-dimensional object or scene. Each point in the point cloud typically includes geometric information, such as three-dimensional coordinates (x, y, z), and attribute information, such as color (R, G, B) and reflectivity.

At present, in an Audio Video coding Standard (AVS), geometric information of a point cloud is encoded first, and after the geometric encoding is completed and the point cloud is geometrically reconstructed, attribute encoding is performed on attribute information of the point cloud, which causes a large time delay on the attribute encoding of the point cloud.

In the process of geometrically encoding the point cloud, multi-branch tree encoding is required to be performed on the point cloud, and the multi-branch tree encoding includes but is not limited to octree encoding, quadtree encoding and binary tree encoding; that is, after the point cloud is divided into complete multi-way trees, the geometric information of the point cloud can be obtained, which causes a great time delay to the geometric coding of the point cloud. In addition, the point cloud decoding process is consistent with the point cloud encoding process, and a large time delay exists.

Based on the above content, the point cloud encoding and decoding process has higher time delay, and further the point cloud encoding and decoding efficiency is reduced.

Disclosure of Invention

The embodiment of the application provides a point cloud encoding method, a point cloud decoding method and a terminal, and can solve the problem that the point cloud encoding and decoding process has higher time delay, so that the point cloud encoding and decoding efficiency is reduced.

In a first aspect, a point cloud encoding method is provided, which includes:

acquiring a first identification parameter of a first target point cloud to be coded;

performing an encoding operation on the first target point cloud based on the first identification parameter;

wherein the encoding operation comprises at least one of:

under the condition that the first identification parameter is used for representing parallel coding, performing geometric coding and attribute prediction coding on the first target point cloud in parallel to obtain a coding result of the first target point cloud;

and performing geometric prediction coding on at least part of points to be coded of the first target point cloud.

In a second aspect, a point cloud decoding method is provided, which includes:

acquiring a fifth identification parameter of a second target point cloud to be decoded;

performing a decoding operation on the second target point cloud based on the fifth identification parameter;

wherein the decoding operation comprises at least one of:

under the condition that the fifth identification parameter is used for representing parallel decoding, performing geometric decoding and attribute predictive decoding on the second target point cloud in parallel to obtain a decoding result of the second target point cloud;

performing geometric predictive decoding on at least a portion of points to be decoded of the second target point cloud.

In a third aspect, an encoder is provided, which includes:

the first acquisition module is used for acquiring a first identification parameter of a first target point cloud to be encoded;

an encoding module to perform an encoding operation on the first target point cloud based on the first identification parameter;

wherein the encoding operation comprises at least one of:

In a fourth aspect, there is provided a decoder comprising:

the second acquisition module is used for acquiring a fifth identification parameter of the second target point cloud to be decoded;

a decoding module for performing a decoding operation on the second target point cloud based on the fifth identification parameter;

wherein the decoding operation comprises at least one of:

In a fifth aspect, there is provided a terminal comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method according to the first aspect or implementing the steps of the method according to the second aspect.

In a sixth aspect, there is provided a readable storage medium on which a program or instructions are stored, which program or instructions, when executed by a processor, performs the steps of the method according to the first aspect, or performs the steps of the method according to the second aspect.

In a seventh aspect, a chip is provided, the chip comprising a processor and a communication interface, the communication interface being coupled to the processor, the processor being configured to execute a program or instructions to implement the method according to the first aspect or to implement the steps of the method according to the second aspect.

In an eighth aspect, there is provided a computer program/program product stored on a non-volatile storage medium, the program/program product being executable by at least one processor to perform the steps of the method according to the first aspect or to perform the steps of the method according to the second aspect.

In the embodiment of the application, geometric coding and attribute prediction coding are executed on the first target point cloud in parallel based on the first identification parameter of the first target point cloud, so that the time delay of the first target point cloud in the attribute coding process is reduced. The time delay of the first target point cloud in the geometric coding process is further reduced by executing geometric predictive coding on at least part of points to be coded of the first target point cloud instead of executing multi-branch tree coding on all the points to be coded of the first target point cloud. Therefore, the time delay of the first target point cloud in the encoding process is reduced, and the encoding efficiency of the first target point cloud is improved.

Drawings

FIG. 1 is a schematic diagram of a point cloud AVS encoder framework;

FIG. 2 is a schematic diagram of a point cloud AVS decoder framework;

FIG. 3 is a flowchart of a point cloud encoding method provided in an embodiment of the present application;

FIG. 4 is a schematic flow chart of parallel encoding provided by an embodiment of the present application;

fig. 5 is a schematic flowchart of low latency geometric predictive coding according to an embodiment of the present application;

FIG. 6 is a flow chart of hybrid geometric coding provided by embodiments of the present application;

FIG. 7 is a flowchart of a point cloud decoding method provided in an embodiment of the present application;

FIG. 8 is a block diagram of an encoder provided in an embodiment of the present application;

fig. 9 is a block diagram of a decoder provided in an embodiment of the present application;

fig. 10 is a block diagram of a communication device provided in an embodiment of the present application;

fig. 11 is a schematic hardware structure diagram of a terminal according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below clearly with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments that can be derived from the embodiments given herein by a person of ordinary skill in the art are intended to be within the scope of the present disclosure.

The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in other sequences than those illustrated or otherwise described herein, and that the terms "first" and "second" used herein generally refer to a class and do not limit the number of objects, for example, a first object can be one or more. In addition, "and/or" in the specification and the claims means at least one of connected objects, and a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.

In an embodiment of the present application, an encoder corresponding to a point cloud encoding method and a decoder corresponding to a point cloud decoding method may both be a terminal, where the terminal may also be referred to as a terminal Device or a User Equipment (UE), and the terminal may be a Mobile phone, a Tablet Personal Computer (Tablet Personal Computer), a Laptop Computer (Laptop Computer) or a notebook Computer, a Personal Digital Assistant (PDA), a palmtop 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 (Wearable Device) or a vehicle-mounted Device (VUE), a pedestrian terminal (PUE), and other terminal side devices, and the Wearable Device includes: smart watches, bracelets, earphones, glasses, and the like. It should be noted that the embodiment of the present application does not limit the specific type of the terminal.

For ease of understanding, some of the contents of the embodiments of the present application are described below:

referring to fig. 1, as shown in fig. 1, in the digital audio video coding and decoding technology standard, a point cloud AVS encoder is currently used to encode the geometric information and the attribute information of a point cloud separately. Firstly, coordinate transformation is carried out on geometric information, so that point clouds are all contained in a bounding box (bounding box), and then coordinate quantization is carried out. The quantization mainly plays a role of scaling, as the quantization can be used for rounding the geometric coordinates, the geometric information of a part of points is the same and is called as a repetition point, whether the repetition point is removed or not is determined according to the parameters, and the two steps of quantizing and removing the repetition point are also called as a voxelization process. Next, the bounding box is partitioned into a multi-tree, e.g., an octree, a quadtree, or a binary tree. In a geometric information coding framework based on a multi-branch tree, a bounding box is divided into 8 subcubes in an eighth-degree mode, the non-empty subcubes are continuously divided until a unit cube with leaf nodes of 1x1x1 is obtained through division, division is stopped, the number of points in the leaf nodes is coded, and a binary code stream is generated.

In the multi-branch tree-based geometric coding of the point cloud, the to-be-coded points need to store the position occupying information of the neighbor nodes to perform predictive coding on the position occupying information of the to-be-coded points, so that for the to-be-coded points close to leaf nodes, a large amount of position occupying information needs to be stored, and a large amount of memory space is occupied.

After the geometric encoding is completed, the geometric information is reconstructed for later recoloring. Attribute coding is primarily directed to color and reflectivity information. Firstly, whether color space conversion is carried out or not is judged according to the parameters, and if the color space conversion is carried out, the color information is converted into a luminance and color (YUV) color space from a Red Green Blue (RGB) color space. Then, recoloring the geometrically reconstructed point cloud by using the original point cloud so as to enable the uncoded attribute information to correspond to the reconstructed geometrical information. In color information coding, after dot clouds are sequenced through Morton codes, the nearest neighbor of a point to be predicted is searched by using a geometric spatial relationship, the point to be predicted is predicted by using a reconstructed attribute value of the found neighbor to obtain a predicted attribute value, then a prediction residual error is obtained by differentiating a real attribute value and the predicted attribute value, finally the prediction residual error is quantized and coded, and a binary code stream is generated.

It should be understood that the decoding process in the digital audio/video codec technology standard corresponds to the above encoding process, and specifically, the AVS decoder framework is shown in fig. 2.

At present, the digital audio and video coding and decoding technical standard has the following technical problems:

firstly, the point cloud can be subjected to attribute coding only after the geometric coding of the point cloud is completed, so that the attribute coding time delay of the point cloud is long.

Secondly, in the process of geometrical coding of the point cloud, geometrical information corresponding to the coding points in the point cloud can be obtained only after the point cloud is subjected to complete multi-branch tree division, so that the time delay of the geometrical coding of the point cloud is long.

Thirdly, in the process of decoding the point cloud, the geometric decoding process and the attribute decoding process have the problem of long time delay for the same reason.

Fourthly, in the geometrical encoding process of the point cloud, the to-be-encoded points need to store the occupation information of the neighbor nodes, and a large amount of memory space is occupied.

Based on the above situation, how to reduce the time delay of the point cloud in the encoding and decoding process, improve the encoding and decoding efficiency, and reduce the memory occupied by the geometric coding is a technical problem to be solved. Based on the point cloud encoding method and the point cloud decoding method, the application provides a point cloud encoding method and a point cloud decoding method.

The point cloud encoding method provided by the embodiments of the present application is described in detail below with reference to the accompanying drawings by using some embodiments and application scenarios thereof.

Referring to fig. 3, fig. 3 is a flowchart of a point cloud encoding method provided in the present application. The point cloud encoding method provided by the embodiment comprises the following steps:

s101, a first identification parameter of a first target point cloud to be coded is obtained.

In this step, the point cloud to be encoded is referred to as a first target point cloud, and it should be understood that, in the process of encoding the point cloud, a frame of point cloud is usually encoded, and here, the first target point cloud may be understood as a frame of point cloud; and the point cloud is a set of randomly distributed discrete points in space that express the spatial structure and surface attributes of the three-dimensional object or scene, that is, the point cloud includes a plurality of encoded points.

The first identification parameter is one of Sequence Parameter Sets (SPS) corresponding to the first target point cloud. For example, the first identification parameter may be geometry _ attribute _ hierarchical _ enable _ flag. It should be understood that the sequence parameter set refers to a parameter set corresponding to a point cloud sequence, and the point cloud sequence refers to a sequence formed by multiple frames of point clouds, where the first identification parameter may be obtained from a sequence parameter set corresponding to a point cloud sequence to which the first target point cloud belongs.

S102, performing encoding operation on the first target point cloud based on the first identification parameter.

In this step, if the first identification parameter exists in the sequence parameter set, parallel encoding may be performed on the first target point cloud, where the following encoding conditions exist in the parallel encoding:

in the first case, geometric coding and attribute predictive coding are executed in parallel on the first target point cloud to obtain a coding result of the first target point cloud.

The geometric coding means performing multi-branch tree coding on the first target point cloud; the attribute prediction coding refers to performing attribute coding on a first target point cloud by determining an attribute prediction mode corresponding to the first target point cloud, wherein please refer to the following embodiments for a specific implementation manner of performing attribute prediction coding on the first target point cloud; the encoding result comprises geometric entropy encoding and attribute entropy encoding corresponding to the first target point cloud.

In the second case, geometric coding and attribute predictive coding are performed in parallel on the first target point cloud, wherein the geometric predictive coding is performed on at least part of the points to be coded of the first target point cloud.

In this case, conventional octree coding may be performed on a part of points to be coded in the first target point cloud, and geometric predictive coding may be performed on another part of points to be coded; or, performing geometric prediction coding on all points to be coded in the first target point cloud.

To facilitate understanding of the above two cases, please refer to fig. 4, where fig. 4 is a schematic flow chart of parallel encoding according to an embodiment of the present application. As shown in fig. 4, determining whether a first identification parameter exists in the sequence parameter set, and if the first identification parameter exists in the sequence parameter set, performing geometric encoding and attribute prediction encoding on the first target point cloud in parallel; if the first identification parameter does not exist in the sequence parameter set, geometric coding is performed on the first target point cloud, and then attribute coding is performed on the first target point cloud.

In the third case, geometric predictive coding is performed on at least part of points to be coded of the first target point cloud, and then attribute coding is performed on the first target point cloud.

The geometric predictive coding is to perform geometric coding on the first target point cloud by determining a geometric predictive mode corresponding to the first target point cloud, wherein please refer to the following embodiments for specific implementation of performing geometric predictive coding on the first target point cloud.

It should be understood that in the process of performing geometric predictive coding on the point to be coded, the point to be coded does not need to be subjected to multi-way tree coding, so that the coding time delay of the first target point cloud is reduced. And the point to be coded needs to store the occupation information of the neighbor node, thereby reducing the memory occupied by the geometric coding.

The following specifically describes a case of performing geometric predictive coding on all points to be coded of the first target point cloud:

optionally, the performing geometric prediction encoding on at least part of the points to be encoded of the first target point cloud comprises:

under the condition that a second identification parameter corresponding to the first target point cloud is used for representing that geometric prediction coding is performed on all points to be coded, determining N geometric prediction values based on a coding sequence corresponding to the points to be coded of the first target point cloud;

determining a rate distortion cost corresponding to each geometric prediction mode;

quantizing a first prediction residual corresponding to the target geometric prediction mode by using the first parameter value;

entropy encoding the quantized first prediction residual.

It should be understood that the sequence parameter set includes a geometry parameter set (SPS) and an Attribute Parameter Set (APS), wherein parameters in the geometry parameter set are related to a geometry encoding process of the point cloud and parameters in the attribute parameter set are related to an attribute encoding process of the point cloud.

In this embodiment, the second identification parameter is a parameter in the geometric parameter set, and in some embodiments, the second identification parameter may be set to be a low _ latency _ geometry _ enable _ flag, where the second identification parameter is also referred to as a low latency parameter. Under the condition that the second identification parameter exists in the geometric parameter set corresponding to the first target point cloud, geometric prediction encoding is performed on all points to be encoded in the first target point cloud, and a process of performing geometric prediction encoding on all points to be encoded can be called low-delay geometric prediction encoding.

Next, the encoding process of geometric predictive encoding is specifically described.

A first list is established in advance, wherein the first list comprises N geometric prediction values, the N geometric prediction values correspond to N geometric prediction modes one to one, and N is a positive integer larger than 1.

Optionally, the determining N geometric predicted values based on the encoding sequence corresponding to the point to be encoded of the first target point cloud includes at least one of:

presetting the N geometric predicted values under the condition that the coding sequence corresponding to the points to be coded is less than or equal to a preset value;

and determining that the N geometric predicted values are associated with the encoded point in the first target point cloud under the condition that the encoding sequence corresponding to the point to be encoded is greater than the preset value.

If the coding sequence corresponding to the point to be coded is less than or equal to a preset value, N geometric predicted values are preset in the first list, wherein each geometric predicted value is different.

Optionally, the preset value is set to 1. That is, for a point to be coded with the first coding order, the point to be coded is subjected to geometric prediction coding by using N preset geometric prediction values.

And if the coding sequence corresponding to the point to be coded is greater than a preset value, setting a geometric predicted value in the first list according to the geometric information of the coded point.

For example, if the number of N is 4, that is, the first list includes 4 geometric predictors and the coding order of the to-be-coded points is 5, the geometric predictors may be determined using the geometric information of 4 to-be-coded points located before the to-be-coded points and having coding orders of 1 to 4.

For example, the geometric predicted value determination rule may be that the first geometric predicted value is the sum of the geometric information of 4 points to be coded; the second geometric predicted value is the minimum geometric information of 4 points to be coded; the third geometric predicted value is the average value of the geometric information of 4 points to be coded; the fourth geometric predicted value is the difference value between the geometric information of the 4 th point to be coded and the geometric information of the 3 rd point to be coded. The geometric information of the point to be encoded can be represented as three-dimensional coordinates (x, y, z) of the point to be encoded.

It should be understood that the specific determination rule regarding the geometric predicted value can be flexibly set, and the embodiment is not particularly limited herein.

As described above, the N geometric prediction values correspond to the N geometric prediction modes one to one, that is, each geometric prediction value is used to characterize one geometric prediction mode. And performing geometric prediction coding on the points to be coded by using N geometric prediction modes, and determining the rate-distortion cost corresponding to each geometric prediction mode.

Specifically, after geometric prediction coding is performed on a point to be coded by using a geometric prediction mode, prediction geometric information corresponding to the point to be coded is obtained, and the prediction geometric information can be understood as a three-dimensional coordinate; and taking the predicted geometric information as the input of a rate-distortion cost algorithm, and calculating to obtain the rate-distortion cost of the point to be coded in the geometric prediction mode.

And after the rate distortion cost corresponding to each geometric prediction mode is obtained, determining the geometric prediction mode with the minimum rate distortion cost as the target geometric prediction mode.

The set of geometry parameters may have a third identification parameter and a first parameter value associated with the third identification parameter. The third identification parameter may be represented as geometry _ enable _ quantized _ flag, and the first parameter value may be represented as GeomQP [3]. The third identification parameter is used to characterize lossy coding, that is, if the third identification parameter exists in the geometric parameter set, it indicates that intra-loop geometric quantization is introduced to the point to be coded. Here, the intra-loop geometric quantization may be understood as quantizing a prediction residual generated by geometrically encoding a point to be encoded.

In this embodiment, under the condition that the third identification parameter exists in the geometric parameter set, the first parameter value is used to quantize the first prediction residual corresponding to the target geometric prediction mode, and entropy coding is performed on the quantized first prediction residual, so as to obtain geometric entropy coding. The first prediction residual can be understood as a difference value between a geometric prediction coding point and a point to be coded, wherein the geometric prediction coding point is a coding point obtained by performing geometric prediction coding on the point to be coded by using a target geometric prediction mode.

It should be understood that, in some embodiments, if the third identification parameter does not exist in the geometric parameter set, the entropy encoding is directly performed on the first prediction residual corresponding to the target geometric prediction mode, so as to obtain the geometric entropy encoding.

In this embodiment, under the condition that the second identification parameter exists in the geometric parameter set, geometric prediction encoding is performed on all points to be encoded in the first target point cloud, and because the geometric prediction encoding does not involve performing multi-way tree division on the points to be encoded, encoding time delay of the first target point cloud can be reduced.

To facilitate understanding of a specific process of performing geometric predictive coding on all points to be coded, please refer to fig. 5, where fig. 5 is a schematic flowchart of low-latency geometric predictive coding according to an embodiment of the present application.

As shown in fig. 5, in the case that the second identification parameter does not exist in the geometric parameter set, the first target point cloud is subjected to the multi-branch tree coding, and the coding result of the multi-branch tree coding is subjected to entropy coding, so as to obtain the geometric entropy coding.

As shown in fig. 5, in the case that the second identification parameter exists in the geometric parameter set, geometric prediction encoding is performed on the first target point cloud, and if a third identification parameter also exists in the geometric parameter set, the prediction residual obtained by the geometric prediction encoding is quantized by using the first parameter value associated with the third identification parameter, so as to obtain a quantized prediction residual, and entropy encoding is performed on the quantized prediction residual, so as to obtain geometric entropy encoding. And if the third identification parameter does not exist in the geometric parameter set, directly performing entropy coding on the prediction residual error to obtain geometric entropy coding.

It should be understood that, in some embodiments, for the requirement of improving the encoding efficiency, the points to be encoded of the first target point cloud may be subjected to a preset ordering, the encoding order of the points to be encoded is determined, and then, each point to be encoded is subjected to geometric prediction encoding.

For example, the encoding order of the points to be encoded can be determined by performing morton code sorting, hilbert sorting or azimuth sequence sorting on the points to be encoded in advance.

The following specifically describes a case of performing geometric predictive coding on part of points to be coded in the first target point cloud:

under the condition that a fourth identification parameter corresponding to the first target point cloud is used for representing mixed coding, acquiring a second parameter value associated with the fourth identification parameter;

dividing the first target point cloud into a first point to be coded and a second point to be coded based on the second parameter value;

and encoding the first point to be encoded and the second point to be encoded by using different encoding modes.

It is to be understood that there may be a fourth identification parameter in the set of geometric parameters and a second parameter value associated with the fourth identification parameter. The fourth identification parameter may be represented as a geometry _ enable _ prediction _ flag, and is also referred to as a hybrid coding parameter, where the fourth identification parameter is used to characterize hybrid coding, that is, if the fourth identification parameter exists in the geometric parameter set, then performing multi-way tree coding on part of points to be coded in the first target point cloud, and performing geometric prediction coding on another part of points to be coded. This second parameter value may be denoted octree _ division _ end _ nodeSizeLog2[3].

And under the condition that the fourth identification parameter exists in the geometric parameter set corresponding to the first target point cloud, performing hybrid coding.

Hereinafter, the hybrid coding will be described in detail.

And acquiring a second parameter value associated with the fourth identification parameter in the geometric parameter set, and dividing the points to be coded in the first target point cloud into first points to be coded and second points to be coded by using the second parameter value.

Optionally, the dividing the first target point cloud into a first point to be encoded and a second point to be encoded based on the second parameter value includes:

determining the point to be coded corresponding to the 1 st coding layer to the M-1 st coding layer of the first target point cloud as the first point to be coded;

and determining the point to be coded corresponding to the Mth coding layer to the Lth coding layer of the first target point cloud as the second point to be coded.

It is to be understood that the first target point cloud comprises L encoding layers, the second parameter value is used to indicate the mth encoding layer, L is a positive integer greater than 1, and M is a positive integer less than L.

For ease of understanding, the examples are illustrated as follows:

the first target point cloud includes 10 encoding layers, i.e., L is 10; the second parameter value is used to indicate the 5 th coding layer, i.e. M is 5. Under the condition, determining the points to be coded corresponding to the 1 st coding layer to the 4 th coding layer of the first target point cloud as first points to be coded; and determining the point to be coded corresponding to the 5 th coding layer to the 10 th coding layer of the first target point cloud as a second point to be coded. The first point to be coded is also called a high-bit point to be coded, and the second point to be coded is also called a low-bit point to be coded.

After the points to be coded in the first target point cloud are divided into a first point to be coded and a second point to be coded, the first point to be coded and the second point to be coded are coded by using different coding modes.

Optionally, the encoding the first point to be encoded and the second point to be encoded by using different encoding modes includes:

performing multi-branch tree coding on the first point to be coded and performing geometric prediction coding on the second point to be coded;

and carrying out geometric predictive coding on the first point to be coded and carrying out multi-branch tree coding on the second point to be coded.

In this embodiment, a first point to be encoded is subjected to multi-branch tree encoding, where the multi-branch tree encoding includes, but is not limited to, octree encoding, quadtree encoding, and binary tree encoding.

The geometric predictive coding is performed on the second point to be coded, and the details of the geometric predictive coding refer to the above embodiments, which are not repeated herein.

In another possible embodiment, the first point to be coded may be subjected to geometric predictive coding, and the second point to be coded may be subjected to multi-way tree coding.

In this embodiment, under the condition that the fourth identification parameter exists in the geometric parameter set corresponding to the first target point cloud, geometric prediction encoding is performed on part of points to be encoded in the first target point cloud, and for the part of points to be encoded, division of a multi-way tree is not required, so that encoding delay of the part of points to be encoded in the geometric encoding process is reduced, and encoding efficiency is further improved.

To facilitate understanding of the process of performing hybrid encoding on the first target point cloud, please refer to fig. 6, and fig. 6 is a schematic flowchart of hybrid geometric encoding according to an embodiment of the present disclosure. As shown in fig. 6, if the fourth identification parameter does not exist in the geometric parameter set, the first target point cloud is subjected to the multi-branch tree coding, and the coding result of the multi-branch tree coding is subjected to entropy coding, so as to obtain the geometric entropy coding.

If the geometric parameter set has a fourth identification parameter, acquiring a second parameter value associated with the fourth identification parameter in the geometric parameter set, dividing the point to be coded of the first target point cloud into a first point to be coded and a second point to be coded by using the second parameter value, and performing multi-way tree coding on the first point to be coded; and executing geometric predictive coding on the second point to be coded, and performing entropy coding on a prediction residual error obtained by the geometric predictive coding to obtain geometric entropy coding.

It should be understood that, in some embodiments, if the first identification parameter exists in the geometric parameter set, but the second identification parameter and the fourth identification parameter do not exist, the multi-tree coding and the attribute prediction coding are synchronously performed on the point to be coded.

Next, the encoding process of the attribute prediction encoding is specifically described.

Optionally, performing attribute prediction encoding on the first target point cloud comprises:

determining I attribute predicted values based on a coding sequence corresponding to a point to be coded of the first target point cloud;

determining a rate distortion cost corresponding to each attribute prediction mode;

and entropy coding a second prediction residual corresponding to a target attribute prediction mode, wherein the target attribute prediction mode is an attribute prediction mode corresponding to the minimum rate distortion cost.

In this embodiment, a second list is pre-established, where the second list includes I attribute prediction values, where the I attribute prediction values are in one-to-one correspondence with the I attribute prediction modes, and I is a positive integer greater than 1.

Specifically, the I attribute prediction values may be determined based on a coding sequence corresponding to a point to be coded.

Optionally, the I attribute prediction values are preset when the coding sequence corresponding to the point to be coded is less than or equal to a preset value.

Illustratively, the preset value may be 1. Therefore, the I attribute predicted values corresponding to the to-be-coded points with the coding sequence of 1 are all preset, wherein the preset I attribute predicted values are different.

Optionally, when the encoding order corresponding to the to-be-encoded point is greater than a preset value, the I attribute prediction values may be determined based on the attribute information of the encoded point in the first target point cloud.

Illustratively, the number of the preset values 1,I is 4, that is, the second list includes 4 attribute prediction values, and the encoding sequence of the points to be encoded is 5; the attribute prediction value may be determined using the attribute information of 4 points to be encoded, which are located before the points to be encoded and have an encoding order of 1 to 4.

The determination rule of the attribute predicted value is the same as the determination rule of the geometric predicted value, and is not repeated herein, and the specific determination rule of the attribute predicted value can be flexibly set, and is not specifically limited herein.

As described above, the I attribute prediction values correspond to the I attribute prediction modes one to one, that is, each attribute prediction value is used to represent one attribute prediction mode. And performing attribute prediction coding on the points to be coded by using the I attribute prediction modes, and determining the rate distortion cost corresponding to each attribute prediction mode. It should be understood that the specific manner of performing attribute predictive coding on the point to be coded is the same as the manner of performing geometric predictive coding on the point to be coded, and will not be repeated here.

Further, the attribute prediction mode with the minimum rate distortion cost is determined as a target attribute prediction mode, and entropy coding is performed on a second prediction residual corresponding to the target attribute prediction mode to obtain attribute entropy coding.

The second prediction residual can be understood as a difference value between an attribute prediction coding point and a point to be coded, where the attribute prediction coding point is a coding point obtained by performing attribute prediction coding on the point to be coded by using a target attribute prediction mode.

In this embodiment, the point to be encoded is encoded by using the attribute predictive encoding, so as to obtain the attribute entropy encoding corresponding to the point to be encoded. Therefore, the attribute information corresponding to the point to be encoded can be obtained without using geometric information, so that the time delay of the attribute encoding process is greatly reduced, and the point cloud encoding efficiency is improved.

In some possible embodiments, the attribute prediction encoding may also be performed on the first target point cloud in the following manner.

Optionally, performing attribute-prediction encoding on the first target point cloud comprises:

determining a target coding point corresponding to the point to be coded based on the geometric information corresponding to the point to be coded of the first target point cloud; the target coding point is a coded point in the first target point cloud;

determining I attribute predicted values corresponding to the to-be-coded points according to the attribute information corresponding to the target coding point;

In this embodiment, according to the coding sequence corresponding to each coding point in the first target point cloud, geometric coding is performed on a part of the coding points in advance to obtain geometric information of the part of the coding points. Subsequently, attribute prediction encoding is performed on the first target point cloud. It should be appreciated that in performing the attribute predictive encoding on the first target point cloud, the first target point cloud is geometrically encoded in parallel. That is, before performing geometric encoding and attribute predictive encoding in parallel on the first target point cloud, geometric information of partial encoding points has been obtained.

Before performing attribute predictive coding on the points to be coded, the geometric information and attribute information corresponding to all the coding points with the coding sequence before the points to be coded are obtained, and the coding process of the part of the coding points is completed, so that the part of the coding points can be called coded points. That is, the encoding points in the encoding order before the encoding are all encoded points.

When the attribute predictive coding is performed on the point to be coded, the geometric information corresponding to the point to be coded and the geometric information corresponding to the coded point can be obtained, and the coded point matched with the geometric information corresponding to the point to be coded is determined as the target coding point.

As mentioned above, the geometric information may be understood as three-dimensional coordinates, and in an alternative embodiment, the three-dimensional coordinates corresponding to the points to be encoded are used as a search center, and the encoded points are searched within a preset range of the search center. If the number of the coded points is 1, determining the coded points as target coded points; and if a plurality of encoded points exist, calculating the Euclidean distance between the three-dimensional coordinate corresponding to each encoded point and the search center, and determining the encoded point with the shortest Euclidean distance as the target encoded point.

It should be understood that the target encoding point may also be determined in other ways, and the above is merely an example.

As described above, before performing the attribute prediction encoding, the second list is established in advance, and the second list includes I attribute prediction values.

For example, if I is 3, that is, the second list includes 3 attribute prediction values, the first attribute prediction value may be set as the color information corresponding to the target coding point, the second attribute prediction value may be set as the reflectivity corresponding to the target coding point, and the third attribute prediction value may be set as the product of the color information corresponding to the target coding point and the reflectivity.

It should be understood that the above is only an example, and the present embodiment does not limit the specific determination rule of the attribute prediction value.

As described above, each attribute predictor is used to characterize an attribute prediction mode. And performing attribute prediction coding on the point to be coded by using the I attribute prediction modes, and determining the rate distortion cost corresponding to each attribute prediction mode. And then determining the attribute prediction mode with the minimum rate distortion cost as a target attribute prediction mode, and performing entropy coding on a second prediction residual corresponding to the target attribute prediction mode to obtain attribute entropy coding. It should be understood that, in the specific process of performing attribute prediction encoding on a point to be encoded by using an attribute prediction value, reference may be made to the foregoing embodiment, and details are not repeated here.

It should be noted that, if the three-dimensional coordinate points represented by the two encoding points are relatively close, there is a correlation between the attribute information of the two encoding points.

In this embodiment, when performing attribute prediction coding on a point to be coded, geometric information corresponding to the point to be coded, and geometric information and attribute information corresponding to a coded point may be obtained. And determining a target coding point corresponding to the point to be coded based on the geometric information of the point to be coded and the geometric information of the coded point, wherein it is understood that the three-dimensional coordinate point represented by the point to be coded is closer to the three-dimensional coordinate point represented by the target coding point.

Furthermore, as described above, there is a correlation between the attribute information of two encoding points with closer three-dimensional coordinate points, and since the three-dimensional coordinate point represented by the point to be encoded and the three-dimensional coordinate point represented by the target encoding point are closer, the attribute prediction encoding is performed on the point to be encoded by using the attribute information of the target encoding point, so as to improve the encoding efficiency of the attribute prediction encoding.

The point cloud decoding method provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings by using some embodiments and application scenarios thereof.

Referring to fig. 7, fig. 7 is a flowchart of a point cloud decoding method provided in the present application. The point cloud decoding method provided by the embodiment comprises the following steps:

s201, acquiring a fifth identification parameter of the second target point cloud to be decoded.

In this step, the point cloud to be decoded is called a second target point cloud, the fifth identification parameter may be the same identification parameter as the first identification parameter, and the fifth identification parameter is obtained from the sequence parameter set corresponding to the second target point cloud.

S202, decoding operation is carried out on the second target point cloud based on the fifth identification parameter.

In this step, if the fifth identification parameter exists in the sequence parameter set, parallel decoding may be performed on the second target point cloud, and in this case, the following decoding situations exist:

in the first case, geometric decoding and attribute predictive decoding are performed on the second target point cloud in parallel to obtain the encoding result of the second target point cloud.

The geometric decoding is to perform multi-branch tree decoding on the second target point cloud. The attribute prediction decoding refers to performing attribute decoding on the second target point cloud by determining an attribute prediction mode corresponding to the second target point cloud, and it should be understood that the manner of determining the attribute prediction mode corresponding to the second target point cloud is the same as the manner of determining the attribute prediction mode corresponding to the first target point cloud. The decoding result includes geometric information and attribute information.

In the second case, geometric predictive decoding is performed on at least part of points to be decoded of the second target point cloud, and then attribute decoding is performed on the second target point cloud.

The geometric prediction decoding is to perform geometric decoding on the second target point cloud by determining a geometric prediction mode corresponding to the second target point cloud, and it should be understood that the manner of determining the geometric prediction mode corresponding to the second target point cloud is the same as the manner of determining the geometric prediction mode corresponding to the first target point cloud.

In a third case, geometric decoding and attribute predictive decoding are performed in parallel on the second target point cloud, wherein the geometric predictive decoding is performed on at least part of the points to be decoded of the second target point cloud.

In this embodiment, based on the fifth identification parameter, geometric decoding and attribute prediction decoding are performed on the second target point cloud in parallel, so as to reduce the time delay of the second target point cloud in the attribute decoding process. And performing geometric prediction decoding on at least part of points to be decoded of the second target point cloud, and further reducing the time delay of the second target point cloud in the geometric decoding process. By the method, the time delay of the second target point cloud in the whole decoding process is reduced, and the decoding efficiency of the second target point cloud is improved.

It should be understood that, in some embodiments, if the second identification parameter exists in the geometric parameter set corresponding to the second target point cloud, geometric prediction decoding may be performed on all the points to be decoded in the second target point cloud.

It should be understood that, in some embodiments, in the process of performing geometric prediction decoding on the second target point cloud, if the third identification parameter and the first parameter value exist in the geometric parameter set, the first parameter value may be used to perform lossy decoding on the point to be decoded.

It should be understood that, in some embodiments, if the fourth identification parameter exists in the geometric parameter set, hybrid decoding is performed on the points to be decoded in the second target point cloud, that is, geometric prediction decoding is performed on part of the points to be decoded, and multi-way tree decoding is performed on another part of the points to be decoded.

It should be noted that, in the point cloud encoding method provided in the embodiment of the present application, the execution main body may be an encoder, or a control module in the encoder for executing the point cloud encoding method. In the embodiment of the present application, an encoder executes a point cloud encoding method as an example, and the encoder provided in the embodiment of the present application is described.

As shown in fig. 8, the encoder 300 includes:

a first obtaining module 301, configured to obtain a first identification parameter of a first target point cloud to be encoded;

an encoding module 302 configured to perform an encoding operation on the first target point cloud based on the first identification parameter.

Optionally, the encoding module 302 includes:

the first determining unit is used for determining N geometric predicted values based on a coding sequence corresponding to the points to be coded of the first target point cloud under the condition that the second identification parameters corresponding to the first target point cloud are used for representing the execution of geometric prediction coding on all the points to be coded;

a second determining unit, configured to determine a rate-distortion cost corresponding to each of the geometric prediction modes;

a quantization unit, configured to quantize a first prediction residual corresponding to the target geometric prediction mode using the first parameter value;

a first encoding unit for entropy encoding the quantized first prediction residual.

Optionally, the first determining unit is specifically configured to:

Optionally, the encoding module 302 includes:

the acquisition unit is used for acquiring a second parameter value associated with a fourth identification parameter under the condition that the fourth identification parameter corresponding to the first target point cloud is used for representing mixed coding;

the dividing unit is used for dividing the first target point cloud into a first point to be coded and a second point to be coded based on the second parameter value;

and the second coding unit is used for coding the first point to be coded and the second point to be coded by using different coding modes.

Optionally, the second encoding unit is specifically configured to:

performing multi-branch tree coding on the first point to be coded and performing geometric predictive coding on the second point to be coded, or;

Optionally, the dividing unit is specifically configured to:

Optionally, the encoding module 302 is specifically configured to:

and entropy coding a second prediction residual corresponding to the target attribute prediction mode.

Optionally, the encoding module 302 is specifically configured to:

determining a target coding point corresponding to the to-be-coded point of the cloud based on the geometric information corresponding to the to-be-coded point of the first target point cloud;

The encoder provided in the embodiment of the present application can implement each process implemented in the method embodiment of fig. 3, and achieve the same technical effect, and is not described here again to avoid repetition.

It should be noted that, in the point cloud decoding method provided in the embodiment of the present application, the execution subject may be a decoder, or a control module in the decoder for executing the point cloud decoding method. In the embodiment of the present application, a point cloud decoding method performed by a decoder is taken as an example to describe the decoder provided in the embodiment of the present application.

As shown in fig. 9, the decoder 400 includes:

a second obtaining module 401, configured to obtain a fifth identification parameter of the second target point cloud to be decoded;

a decoding module 402 for performing a decoding operation on the second target point cloud based on the fifth identification parameter.

In the embodiment of the application, geometric coding and attribute prediction coding are executed on the first target point cloud in parallel based on the first identification parameter of the first target point cloud, so that the time delay of the first target point cloud in the attribute coding process is reduced. The time delay of the first target point cloud in the geometric coding process is further reduced by executing geometric predictive coding on at least part of points to be coded of the first target point cloud instead of executing multi-branch tree coding on all points to be coded of the first target point cloud. Therefore, the time delay of the first target point cloud in the encoding process is reduced, and the encoding efficiency of the first target point cloud is improved.

The encoder and the decoder in the embodiments of the present application may be a device, a device or an electronic device having an operating system, or may be a component, an integrated circuit, or a chip in a terminal. The device or the electronic equipment can be a mobile terminal or a non-mobile terminal. For example, the mobile terminal may include, but is not limited to, the type of the terminal 11 listed above, and the non-mobile terminal may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a television (television), a teller machine (teller machine), a self-service machine (kiosk), or the like, and the embodiments of the present application are not limited in particular.

The decoder provided in the embodiment of the present application can implement each process implemented in the method embodiment of fig. 7, and achieve the same technical effect, and is not described herein again to avoid repetition.

Optionally, as shown in fig. 10, an embodiment of the present application further provides a communication device 500, which includes a processor 501, a memory 502, and a program or an instruction stored in the memory 502 and executable on the processor 501, for example, when the communication device 500 is a terminal, the program or the instruction is executed by the processor 501 to implement the processes of the above-mentioned embodiment of the point cloud encoding method, and can achieve the same technical effect, or implement the processes of the above-mentioned embodiment of the point cloud decoding method, and can achieve the same technical effect.

An embodiment of the present application further provides a terminal, including a processor and a communication interface, where the processor is configured to perform the following operations:

wherein the encoding operation comprises at least one of:

Alternatively, the processor is configured to perform the following operations:

wherein the decoding operation comprises at least one of:

The terminal embodiment corresponds to the terminal-side method embodiment, and all implementation processes and implementation modes of the method embodiment can be applied to the terminal embodiment and can achieve the same technical effect. Specifically, fig. 11 is a schematic diagram of a hardware structure of a terminal for implementing the embodiment of the present application.

The terminal 1000 can include, but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010.

Those skilled in the art will appreciate that terminal 1000 can also include a power supply (e.g., a battery) for powering the various components, which can be logically coupled to processor 1010 via a power management system to provide management of charging, discharging, and power consumption via the power management system. The terminal structure shown in fig. 11 does not constitute a limitation of the terminal, and the terminal may include more or less components than those shown, or combine some components, or have a different arrangement of components, and thus will not be described again.

It should be understood that, in the embodiment of the present application, the input Unit 1004 may include a Graphics Processing Unit (GPU) 10041 and a microphone 10042, and the Graphics Processing Unit 10041 processes image data of still pictures or video obtained by an image capturing device (such as a camera) in a video capturing mode or an image capturing mode. The display unit 1006 may include a display panel 10061, and the display panel 10071 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 1007 includes a touch panel 10071 and other input devices 10072. The touch panel 10071 is also referred to as a touch screen. The touch panel 10071 may include two parts, a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein.

In this embodiment, after receiving downlink data from a network side device, the radio frequency unit 1001 processes the downlink data to the processor 1010; in addition, the uplink data is sent to the network side equipment. In general, radio frequency unit 1001 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

The memory 1009 may be used to store software programs or instructions and various data. The memory 1009 may mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area may store an operating system, an application program or instruction (such as a sound playing function, an image playing function, and the like) required for at least one function, and the like. Further, the Memory 1009 may include a high-speed random access Memory and may also include a nonvolatile Memory, where the nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable Programmable PROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), or a flash Memory. Such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.

Processor 1010 may include one or more processing units; alternatively, the processor 1010 may integrate an application processor, which primarily handles operating system, user interface, and applications or instructions, etc., and a modem processor, which primarily handles wireless communications, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into processor 1010.

Wherein the processor is configured to perform the following operations:

wherein the encoding operation comprises at least one of:

Alternatively, the processor is configured to perform the following operations:

wherein the decoding operation comprises at least one of:

The embodiments of the present application further provide a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the above-mentioned point cloud encoding method embodiment or implements each process of the above-mentioned point cloud decoding method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.

Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and so on.

The embodiment of the present application further provides a chip, where 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 an instruction to implement each process of the above-mentioned point cloud encoding method embodiment or each process of the above-mentioned point cloud decoding method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the description is omitted here.

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

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatuses in the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions recited, e.g., the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A point cloud encoding method, comprising:

wherein the encoding operation comprises at least one of:

under the condition that the first identification parameters are used for representing parallel coding, performing geometric coding and attribute prediction coding on the first target point cloud in parallel to obtain a coding result of the first target point cloud;

2. The method of claim 1, wherein the performing geometric predictive encoding of at least some points to be encoded of the first target point cloud comprises:

under the condition that a second identification parameter corresponding to the first target point cloud is used for representing that geometric prediction coding is performed on all points to be coded, determining N geometric prediction values based on a coding sequence corresponding to the points to be coded of the first target point cloud; the coding sequence is determined based on the preset sequencing of the points to be coded, the N geometric prediction values correspond to N geometric prediction modes one by one, and N is a positive integer greater than 1;

quantizing a first prediction residual corresponding to the target geometric prediction mode by using the first parameter value; the first parameter value is associated with a third identification parameter of the first target point cloud, the third identification parameter is used for representing lossy coding, and the target geometric prediction mode is a geometric prediction mode corresponding to the minimum rate-distortion cost;

entropy encoding the quantized first prediction residual.

3. The method of claim 2, wherein the determining N geometric predictors based on the encoding order of the point to be encoded of the first target point cloud comprises at least one of:

4. The method of claim 1, wherein the performing geometric predictive coding on at least some points to be coded of the first target point cloud comprises:

5. The method according to claim 4, wherein said encoding the first point to be encoded and the second point to be encoded using different encoding schemes comprises:

6. The method of claim 4, wherein the first target point cloud comprises L encoded layers, wherein the second parameter value is used to indicate the Mth encoded layer, wherein L is a positive integer greater than 1, and M is a positive integer less than L;

the dividing the first target point cloud into a first point to be encoded and a second point to be encoded based on the second parameter value comprises:

and determining the point to be coded corresponding to the Mth to Lth coding layers of the first target point cloud as the second point to be coded.

7. The method of claim 1, wherein performing attribute prediction encoding on the first target point cloud comprises:

determining I attribute predicted values based on a coding sequence corresponding to a point to be coded of the first target point cloud; the I attribute predicted values correspond to I attribute prediction modes one by one, and I is a positive integer greater than 1;

8. The method of claim 1, wherein performing attribute prediction encoding on the first target point cloud comprises:

determining I attribute predicted values corresponding to the to-be-coded points according to the attribute information corresponding to the target coding point; the I attribute prediction values correspond to the I attribute prediction modes one by one, and I is a positive integer greater than 1;

9. A point cloud decoding method, comprising:

wherein the decoding operation comprises at least one of:

10. An encoder, comprising:

the first acquisition module is used for acquiring a first identification parameter of a first target point cloud to be coded;

wherein the encoding operation comprises at least one of:

11. The encoder of claim 10, wherein the encoding module comprises:

the first determining unit is used for determining N geometric predicted values based on a coding sequence corresponding to the points to be coded of the first target point cloud under the condition that the second identification parameters corresponding to the first target point cloud are used for representing the execution of geometric prediction coding on all the points to be coded; the coding sequence is determined based on the preset sequencing of the points to be coded, the N geometric prediction values correspond to N geometric prediction modes one by one, and N is a positive integer greater than 1;

a quantization unit, configured to quantize a first prediction residual corresponding to the target geometric prediction mode using the first parameter value; the first parameter value is associated with a third identification parameter of the first target point cloud, the third identification parameter is used for representing lossy coding, and the target geometric prediction mode is a geometric prediction mode corresponding to the minimum rate-distortion cost;

12. The encoder according to claim 11, wherein the first determining unit is specifically configured to:

13. The encoder according to claim 10, characterized in that the encoding module comprises:

14. The encoder according to claim 13, wherein the second encoding unit is specifically configured to:

15. The encoder of claim 13, wherein the first target point cloud comprises L encoding layers, wherein the second parameter value is used to indicate the mth encoding layer, wherein L is a positive integer greater than 1, and wherein M is a positive integer less than L;

the dividing unit is specifically configured to:

16. The encoder according to claim 10, characterized in that the encoding module is specifically configured to:

determining I attribute predicted values based on a coding sequence corresponding to a point to be coded of the first target point cloud; the I attribute prediction values correspond to the I attribute prediction modes one by one, and I is a positive integer greater than 1;

and entropy coding a second prediction residual corresponding to a target attribute prediction mode, wherein the target attribute prediction mode is the attribute prediction mode corresponding to the minimum rate distortion cost.

17. The encoder according to claim 10, wherein the encoding module is specifically configured to:

18. A decoder, characterized in that it comprises:

wherein the decoding operation comprises at least one of:

19. A terminal comprising a processor, a memory and a program or instructions stored on the memory and executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the point cloud encoding method of any one of claims 1 to 8 or implementing the steps of the point cloud decoding method of claim 9.

20. A readable storage medium, characterized in that it stores thereon a program or instructions which, when executed by a processor, implement the steps of the point cloud encoding method of any one of claims 1-8, or the steps of the point cloud decoding method of claim 9.