CN115474058A

CN115474058A - Point cloud encoding processing method, point cloud decoding processing method and related equipment

Info

Publication number: CN115474058A
Application number: CN202110656018.7A
Authority: CN
Inventors: 张伟; 陈天; 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: WO2022257971A1

Abstract

The application discloses a point cloud coding processing method, a point cloud decoding processing method and related equipment, which belong to the technical field of point cloud processing, and the point cloud coding processing method of the embodiment of the application comprises the following steps: performing predictive coding based on the geometric information of the point cloud to be coded to obtain geometric predictive residual information; carrying out quantization processing on the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information; and entropy coding is carried out on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream.

Description

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

Technical Field

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

Background

The point cloud is an expression form of a three-dimensional object or scene and is composed of a group of discrete point sets which are randomly distributed in space and express the space structure and surface attributes of the three-dimensional object or scene. In order to accurately reflect the information in the space, the number of the required discrete points is quite large, and in order to reduce the bandwidth occupied by the point cloud data during storage and transmission, the point cloud data needs to be encoded and compressed. The point cloud data is generally composed of geometric information describing a location, such as three-dimensional coordinates (x, y, z), and attribute information of the location, such as color (R, G, B) or reflectance. The geometrical information and the attribute information are coded separately in the point cloud coding and compressing process.

At present, before encoding geometric information of point clouds, rounding and de-weighting pretreatment is carried out on the geometric information, the influence of information source density degree on the geometric information pretreatment is large, for example, when the information source distribution is dense, the quantity of the point clouds is reduced sharply by the pretreatment on the geometric information, the speed of a geometric code stream is greatly influenced by the source density degree, and the speed control effect of the geometric code stream of the point clouds is poor.

Disclosure of Invention

The embodiment of the application provides a point cloud encoding processing method, a point cloud decoding processing method and related equipment, and can solve the problem that the rate of a geometric code stream is greatly influenced by the density degree of an information source, so that the rate control effect of the geometric code stream of the point cloud is poor.

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

performing predictive coding based on the geometric information of the point cloud to be coded to obtain geometric predictive residual information;

quantizing the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information;

and entropy coding is carried out on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream.

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

entropy decoding is carried out on the geometric code stream to obtain quantized geometric prediction residual information;

carrying out inverse quantization processing on the quantized geometric prediction residual error information according to the geometric quantization parameters to obtain geometric prediction residual error information;

and performing predictive decoding based on the geometric prediction residual error information to obtain the geometric information of the point cloud to be decoded.

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

the first coding module is used for carrying out predictive coding on the basis of geometric information of point cloud to be coded to obtain geometric prediction residual error information;

the first quantization module is used for performing quantization processing on the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information;

and the second coding module is used for carrying out entropy coding on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream.

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

the first decoding module is used for carrying out entropy decoding on the geometric code stream to obtain quantized geometric prediction residual error information;

the first inverse quantization module is used for carrying out inverse quantization processing on the quantized geometric prediction residual error information according to the geometric quantization parameter to obtain geometric prediction residual error information;

and the second decoding module is used for carrying out predictive decoding on the basis of the geometric prediction residual error information to obtain the geometric information of the point cloud to be decoded.

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; alternatively, the program or instructions, when executed by the processor, implement the steps of the method according to the second aspect.

In a sixth aspect, a terminal is provided, which includes a processor and a communication interface, where the processor or the communication interface is configured to:

carrying out quantization processing on the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information;

A seventh aspect provides a terminal, including a processor and a communication interface, where the processor or the communication interface is configured to:

carrying out inverse quantization processing on the quantized geometric prediction residual error information according to geometric quantization parameters to obtain geometric prediction residual error information;

In an eighth aspect, a readable storage medium is provided, on which a program or instructions are stored, which when executed by a processor implement the steps of the point cloud encoding processing method according to the first aspect, or which when executed by a processor implement the steps of the point cloud decoding processing method according to the second aspect.

In a ninth 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 steps of the point cloud encoding processing method according to the first aspect or to implement the steps of the point cloud decoding processing method according to the second aspect.

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

In the embodiment of the application, the geometric information of the point cloud to be coded is subjected to predictive coding to obtain geometric prediction residual information; carrying out quantization processing on the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information; and entropy coding is carried out on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream. Therefore, the geometric prediction residual information is quantized through the geometric quantization parameters, the influence of the information source density degree on the geometric code stream rate is reduced, and the rate control effect of the geometric code stream of the point cloud can be improved.

Drawings

FIG. 1 is one of schematic diagrams 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 processing method according to an embodiment of the present disclosure;

FIG. 4 is a second schematic diagram of an AVS encoder frame for point cloud;

FIG. 5 is a second schematic diagram of a point cloud AVS decoder framework;

fig. 6 is a flowchart of a point cloud decoding processing method according to an embodiment of the present disclosure;

fig. 7 is a structural diagram of a point cloud encoding processing apparatus according to an embodiment of the present disclosure;

fig. 8 is a second structural diagram of a point cloud encoding processing apparatus according to an embodiment of the present disclosure;

fig. 9 is a third structural diagram of a point cloud encoding processing apparatus according to an embodiment of the present application;

fig. 10 is a fourth structural diagram of a point cloud encoding processing apparatus according to an embodiment of the present application;

fig. 11 is a structural diagram of a point cloud decoding processing apparatus according to an embodiment of the present disclosure;

fig. 12 is a second structural diagram of a point cloud decoding processing apparatus according to an embodiment of the present application;

fig. 13 is a block diagram of a communication device according to an embodiment of the present application;

fig. 14 is a block 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 claims means at least one of connected objects, and a character "/" generally means that the former and latter related objects are in an "or" relationship.

The encoding and decoding end corresponding to the encoding and decoding method in the embodiment of the present application may be a terminal, which may also be referred to as a terminal Device or a User Equipment (UE), where 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:

as shown in fig. 1, in a point cloud digital Audio and Video coding Standard (AVS) encoder framework, geometric information and attribute information of a point cloud are separately encoded. Firstly, coordinate transformation is carried out on the geometric information, so that the point cloud is completely 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. The current AVS geometric partitioning order includes two types:

1. breadth-first traversal order: when the octree is divided for geometry, the nodes in the same layer are divided firstly, the nodes in the next layer are divided continuously until all the nodes in the current layer are divided, and finally, the division is stopped when the leaf nodes obtained by the division are the unit cube of 1x1x 1.

2. Depth-first traversal order: when the geometry is divided into octree, the first node of the current layer is continuously divided firstly, and the division of the current node is stopped until the leaf node obtained by the division is a unit cube of 1x1x 1. And according to the sequence, dividing the subsequent nodes of the current layer until the nodes on the current layer are divided.

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.

Optionally, the AVS decoding process corresponds to the encoding process, and specifically, the AVS decoder framework is as shown in fig. 2.

It should be noted that the AVS encoding framework may include two stages of preprocessing and encoding, where the processing of the point cloud in the preprocessing stage may be referred to as out-of-loop processing, and the processing of the point cloud in the encoding stage after the preprocessing is completed may be referred to as in-loop processing.

The point cloud encoding processing 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 processing method provided in an embodiment of the present application, and as shown in fig. 3, the point cloud encoding processing method includes the following steps:

step 101, performing predictive coding based on geometric information of a point cloud to be coded to obtain geometric predictive residual error information.

Wherein the geometric information may comprise a geometric position. During predictive coding, a prediction candidate list can be established for the geometric information of the point cloud to be coded, the best geometric prediction value is selected from the prediction candidate list, and the best geometric prediction value and the geometric information are subjected to subtraction to obtain geometric prediction residual information. Each geometric predictor in the prediction candidate list may correspond to one geometric prediction mode. For example, a prediction candidate list may be established in advance, and the prediction candidate list may include N geometric predictors, where the N geometric predictors are in one-to-one correspondence with the N geometric prediction modes, and N is a positive integer greater than 1. For example, 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 5 th point cloud to be encoded in all the point clouds, the geometric prediction values may be determined by using the geometric information of the 4 point clouds to be encoded, which are located before the point cloud to be encoded and have an encoding order of 1 to 4. For example, the geometric predicted value may be determined by the rule that the first geometric predicted value is the sum of the geometric information of 4 point clouds to be encoded; the second geometric predicted value is the minimum geometric information of 4 point clouds to be coded; the third geometric predicted value is the average value of the geometric information of 4 point clouds to be coded; the fourth geometric predicted value is the difference value between the geometric information of the 4 th point cloud to be coded and the geometric information of the 3 rd point cloud to be coded. The geometric information of the point cloud to be encoded can be represented as three-dimensional coordinates (x, y, z) of the point cloud to be encoded.

It should be understood that the specific determination rule regarding the geometric predicted value may be flexibly set, and the embodiment is not limited in this respect.

And 102, carrying out quantization processing on the geometric prediction residual error information according to the geometric quantization parameters to obtain quantized geometric prediction residual error information.

Wherein it may be determined whether the geometric quantization control parameter indicates that quantization processing is enabled; under the condition that the geometric quantization control parameter indicates that quantization processing is started, performing quantization processing on the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information, and performing entropy coding on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream; and under the condition that the geometric quantization control parameter indicates that quantization processing is not started, entropy coding is carried out according to the geometric prediction residual error information to obtain a geometric code stream.

In addition, the geometric quantization parameter may be read from a configuration file, and for example, a value of a parameter geompqp in the configuration file may be read as the geometric quantization parameter.

And 103, entropy coding is carried out based on the quantized geometric prediction residual error information to obtain a geometric code stream.

The quantized geometric prediction residual information and the geometric prediction mode can be entropy-coded to obtain a geometric code stream.

It should be noted that, in the geometric coding scheme based on the octree, the lossy quantization process for the geometric information is performed in the preprocessing, and the lossy quantization of deduplication and rounding is performed on the original point cloud data through the quantization step size in the preprocessing, so that not only is the original point cloud data rounded, but also the duplicate points are removed after rounding, and the lossy quantization is implemented. Lossy quantization in this pre-processing can present the following problems: when the information sources are distributed sparsely and uniformly, the number of quantized points can be reduced uniformly by setting different quantization step lengths, but when the information sources are distributed densely and concentratedly, the number of points is reduced sharply due to the smaller quantization step length; lossy quantization is completed outside the loop, and is realized by quantization step length, namely, the original point cloud data is subjected to one-time down-sampling operation, and the down-sampling point number is influenced by the information source, so that the quantized geometric code stream is also influenced by the information source, and meanwhile, under the condition that the point number is fixed, the coordinates of the point cannot be subjected to further lossy quantization, so that the rate control of the geometric code stream cannot be realized; the preprocessed point cloud is greatly influenced by the density degree of the information source, and the quality of the geometrical information of the preprocessed point cloud cannot be accurately controlled by adjusting the quantization step outside the ring under the condition that the information source is unknown.

In the embodiment of the application, the geometric prediction residual information is quantized through the geometric quantization parameters, and intra-loop lossy quantization is introduced into point cloud coding, so that the influence of the information source density degree on the rate of the geometric code stream is reduced, and the rate control effect of the geometric code stream of the point cloud can be improved.

In the embodiment of the application, the geometric information of the point cloud to be coded is subjected to predictive coding to obtain geometric prediction residual information; quantizing the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information; and entropy coding is carried out on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream. Therefore, the geometric prediction residual information is quantized through the geometric quantization parameters, the influence of the information source density degree on the rate of the geometric code stream is reduced, and the rate control effect of the geometric code stream of the point cloud can be improved.

Optionally, the quantizing the geometric prediction residual information according to the geometric quantization parameter includes:

determining whether the geometric quantization control parameter indicates that quantization processing is enabled;

and in the case that the geometric quantization control parameter indicates that quantization processing is enabled, performing quantization processing on the geometric prediction residual information according to a geometric quantization parameter.

Wherein the geometric quantization control parameter indicates that quantization processing is enabled, which may be considered as indicating that intra-loop lossy quantization is enabled for the geometric prediction residual information. The geometric quantization control parameter may be read from a configuration file, and for example, a value of a parameter geometry _ enable _ quantized _ flag in the configuration file may be read as the geometric quantization control parameter. The parameter geometry _ enable _ quantized _ flag may be a parameter newly introduced in a gps (geometry parameters set) high level syntax element. When the geometric quantization control parameter is configured to 1, it may indicate that the quantization process is enabled; when the geometric quantization control parameter is configured to 0, it may indicate that the quantization process is not enabled.

In this embodiment, whether the geometric prediction residual information is quantized according to the geometric quantization parameter is determined by the geometric quantization control parameter, so that flexibility of encoding the geometric information of the point cloud can be improved.

Optionally, after determining whether the geometric quantization control parameter indicates that the quantization process is enabled, the method further includes:

and under the condition that the geometric quantization control parameter indicates that quantization processing is not started, entropy coding is carried out according to the geometric prediction residual error information to obtain a geometric code stream.

Where the geometric quantization control parameter indicates that no quantization processing is enabled, it may be considered as indicating that no intra-loop lossy quantization is enabled for the geometric prediction residual information.

In this embodiment, in the case that the geometric quantization control parameter indicates that quantization processing is not enabled, entropy encoding is performed according to the geometric prediction residual information, so that whether quantization processing is performed on the geometric prediction residual information in the process of predictive encoding of the geometric information can be determined according to the geometric quantization control parameter, and flexibility of geometric information encoding of the point cloud can be improved.

Optionally, the performing predictive coding on the geometric information of the point cloud to be coded includes:

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 identification corresponding to the point cloud to be coded;

under the condition that 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 coded;

and under the condition that 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 coded.

The point cloud to be encoded can be divided into a first sub point cloud to be encoded and a second sub point cloud to be encoded according to the relationship between the node identification corresponding to the point cloud to be encoded and the preset threshold value. The first sub point cloud to be encoded may be a low-bit point cloud to be encoded, and the second sub point cloud to be encoded may be a high-bit point cloud to be encoded. The geometric information of the high-bit point cloud to be encoded may include octree high-bit coordinates, and the geometric information of the low-bit point cloud to be encoded may include octree low-bit coordinates.

Exemplarily, as shown in fig. 4, for a high-bit point cloud to be encoded, octree construction is performed to implement octree encoding; and performing geometric prediction and residual quantization on the low-bit point cloud to be coded to realize predictive coding. At the decoding end, as shown in fig. 5, high-bit point cloud to be encoded is obtained through octree reconstruction; and obtaining low-bit point cloud to be coded through inverse quantization and geometric reconstruction.

Taking the example that the geometric information of the point cloud to be encoded is represented by the morton code and the geometric information is constructed into the geometric octree through the morton code, the node identification corresponding to the point cloud to be encoded can be the number of encoding layers in the octree encoding process. For example, the preset threshold may be 5, all the point clouds to be encoded may include 10 encoding layers, the point clouds to be encoded corresponding to the 1 st to 4 th encoding layers may be regarded as high-bit point clouds to be encoded, and the point clouds to be encoded corresponding to the 5 th to 10 th encoding layers may be regarded as low-bit point clouds to be encoded.

In addition, the preset threshold may be the value of the parameter octree _ division _ end _ nodeSizeLog2[3 ]. The value of the parameter octree _ division _ end _ nodeSizeLog2[3] may be read from the configuration file as a 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 encoding of the quantized geometric prediction residual information may not be required. When the first coding mode and the preset threshold are matched with 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 smaller than the preset threshold, the geometric prediction residual information is not quantized to 0, and entropy encoding may be performed based on the quantized geometric prediction residual information.

In the embodiment, under the condition that the geometric coding control parameters indicate different coding modes, the point clouds to be coded, which are used for performing predictive coding on the geometric information, are different, and a user can modify the coding modes by setting the geometric coding control parameters, so that the coding modes of the point clouds to be coded are modified, and the flexibility of point cloud coding can be improved.

Optionally, 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 rate distortion costs corresponding to the at least two candidate geometric prediction residual error 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;

entropy encoding is performed based on the target quantized geometric prediction residual information.

Wherein 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. Illustratively, the at least two candidate geometric prediction residual information may include quantized geometric prediction residual information and a fixed value {0,0,0}.

In the embodiment, a rate-distortion optimization algorithm is introduced to process quantized geometric prediction residual information to obtain target quantized geometric prediction residual information, and entropy coding is performed on the basis of the target quantized geometric prediction residual information, so that efficiency of lossy coding on geometric information can be improved.

Optionally, the target quantized geometric prediction residual information is candidate geometric prediction residual information with a minimum rate-distortion cost in the at least two candidate geometric prediction residual information.

Wherein, the at least two candidate geometric prediction residual information can be stored in a form of a candidate list, and a first candidate geometric prediction residual information in the candidate list is used as an optimal candidate geometric prediction residual information; traversing candidate geometric prediction residual error 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 optimal candidate geometric prediction residual information, updating the current candidate geometric prediction residual information into the optimal candidate geometric prediction residual information, otherwise, not updating the optimal candidate geometric prediction residual information; and after traversing the candidate list, determining the best candidate geometric prediction residual error information as target quantization geometric prediction residual error information. After determining the target quantized geometric prediction residual information, the target quantized geometric prediction residual information may be input to an encoder for entropy encoding.

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 quantization geometric prediction residual information, so that the lossy coding process of the geometric information can be optimized, and the point cloud coding efficiency can be improved.

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 code 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 code rate is used to characterize a predicted bit value for encoding the candidate geometric prediction residual information.

The rate-distortion cost corresponding to the candidate geometric prediction residual information may be positively correlated to both the geometric distortion value and the first prediction residual code rate. For example, the rate-distortion cost1 corresponding to the candidate geometric prediction residual information may be:

cost1＝dist1+λ1*rate1

where λ 1 may represent a weight parameter of a code rate and distortion in a rate distortion cost, for example, λ 1 may be set to 0.4,0.5 or 0.6, etc.; rate1 may represent a first prediction residual code rate; dist may represent a geometric distortion value. The calculation formula of the geometric distortion value dist1 can be as follows:

dist1＝normal1(recPos-oriPos)

the function normal1 represents a norm of an expression, recPos represents a geometric coordinate reconstructed by using candidate geometric prediction residual information and a geometric prediction value, and oriPos represents an original geometric coordinate.

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 code rate, so that the rate-distortion cost corresponding to the candidate geometric prediction residual information can be determined more accurately.

Optionally, the at least two pieces of candidate geometric prediction residual information 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;

the entropy encoding based on the target quantized geometric prediction residual information comprises:

entropy encoding is performed on the basis of 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 related to the quantized geometric prediction residual information;

and if the target quantized geometric prediction residual information is candidate geometric prediction residual information irrelevant to the quantized geometric prediction residual information, performing entropy coding based on an identifier corresponding to the target quantized geometric prediction residual information.

Each candidate geometric prediction residual error information can be correspondingly provided with an identifier. The candidate geometric prediction residual information is related to the quantized geometric prediction residual information, and may be obtained based on the quantized geometric prediction residual information, and exemplarily, the candidate geometric prediction residual information is equal to the quantized geometric prediction residual information, and the identifier corresponding to the candidate geometric prediction residual information may be 1; or, the candidate geometric prediction residual information is equal to an integer multiple of the quantized geometric prediction residual information, and the identifier corresponding to the candidate geometric prediction residual information may be 2, and so on; the candidate geometric prediction residual information is not related to the quantized geometric prediction residual information, and may be that the candidate geometric prediction residual information is preset geometric prediction residual information, for example, (0,0,0), and the identifier corresponding to the candidate geometric prediction residual information may be 0.

In addition, a geometric rate distortion optimization control parameter may be set, and if the geometric rate distortion optimization control parameter is a first preset value, the entropy encoding is performed based on the target quantized geometric prediction residual error information, including: entropy encoding is performed on the basis of 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 related to the quantized geometric prediction residual information; and if the target quantized geometric prediction residual information is candidate geometric prediction residual information irrelevant to the quantized geometric prediction residual information, performing entropy coding based on an identifier corresponding to the target quantized geometric prediction residual information.

If the geometric rate distortion optimization control parameter is a second preset value, entropy encoding is performed on the basis of the target quantized geometric prediction residual error information, and the entropy encoding includes: entropy encoding based on the target quantized geometric prediction residual information if the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information; entropy encoding based on the target quantized geometric prediction residual information if the target quantized geometric prediction residual information is candidate geometric prediction residual information not related to the quantized geometric prediction residual information.

The first preset value and the second preset value are not limited in this embodiment. For example, the first preset value may be 1, and the second preset value may be 0.

Further, it may be determined that the target quantized geometric prediction residual information is related or unrelated to the quantized geometric prediction residual information by an identifier corresponding to the target quantized geometric prediction residual information. When the decoding end decodes, an identifier corresponding to the target quantized geometric prediction residual information may be firstly analyzed, and if it is determined that the target quantized geometric prediction residual information is not related to the quantized geometric prediction residual information according to the identifier corresponding to the target quantized geometric prediction residual information, the target quantized geometric prediction residual information may be found according to the identifier corresponding to the target quantized geometric prediction residual information; and if the correlation between the target quantized geometric prediction residual information and the quantized geometric prediction residual information is determined according to the identifier corresponding to the target quantized geometric prediction residual information, decoding the geometric code stream to obtain the target quantized geometric prediction residual information.

In this embodiment, in a case where the target quantized geometric prediction residual information is candidate geometric prediction residual information related to the quantized geometric prediction residual information, entropy encoding is performed based on an identifier corresponding to the target quantized geometric prediction residual information and the target quantized geometric prediction residual information; and if the target quantized geometric prediction residual error information is candidate geometric prediction residual error information irrelevant to the quantized geometric prediction residual error information, performing entropy coding based on the identifier corresponding to the target quantized geometric prediction residual error information. In this way, the partial target quantized geometric prediction residual information may be encoded only with the flag corresponding to the target quantized geometric prediction residual information without being encoded, and the encoding efficiency can be further improved.

obtaining a quantized point cloud corresponding to the point cloud to be encoded according to a preset first quantization step;

carrying out duplicate removal processing on the quantized point cloud;

and performing predictive coding on the geometric information of the point cloud to be coded corresponding to the quantized point cloud obtained after the duplicate removal processing.

Before the quantization point cloud corresponding to the point cloud to be encoded is obtained according to the preset first quantization step, as shown in fig. 4, coordinate translation processing may be performed on the point cloud to be encoded, and the bounding box may be moved to a coordinate origin (0,0,0) by the coordinate translation processing, where the bounding box represents a minimum rectangular parallelepiped containing all points in the input point cloud. The first quantization step size may be preset by a user, and for example, the first quantization step size QS may be:

QS＝2 ⁱ

where i =0 in the lossless case and i =9, 8, 6, 5, 3, 2 in the lossy case according to different quantization levels (r 01, …, r 06), i.e. QS may have a value of 512, 256, 64, 32,8,4,1.

The quantized point cloud corresponding to the point cloud to be encoded may be obtained as follows:

X＝round(x/QS)

Y＝round(y/QS)

Z＝round(z/QS)

wherein, (X, Y, Z) represents the quantized coordinates of the quantized point cloud, (X, Y, Z) represents the coordinates of the original point cloud, QS represents the first quantization step, and a round(s) function represents the integer nearest to the return s, and the specific definition of the function can be as follows:

after the quantized coordinates of the quantized point clouds are obtained through calculation, the quantized coordinates of a plurality of original point clouds are the same, and the original point clouds with the same quantized coordinates are repeated points. The quantized point cloud may be subjected to a deduplication process in a case where a deduplication parameter get _ remove _ dup _ flag indicates the removal of duplicate points. And (4) the point cloud coordinate to be coded corresponding to the quantized point cloud obtained after the duplication removal processing is the original point cloud coordinate, and the point cloud coordinate is not subjected to quantization operation.

In the embodiment, the geometric information of the point cloud to be coded corresponding to the quantized point cloud obtained after the deduplication processing is subjected to predictive coding, so that only the number of point clouds is down-sampled during the out-of-loop quantization, and the point cloud coordinates are not quantized.

determining a first geometric quantization step size according to the geometric quantization parameter;

and quantizing the geometric prediction residual error information based on the first geometric quantization step size and a first preset geometric offset value.

Wherein, the first geometric quantization step QS1 may be:

therein, 2 ^shift1 The first preset geometric deviation value can be represented, shift1 can represent the number of bits of deviation in the quantization processing process, the larger shift1 is, the more accurate the representation quantization result is, and QP1 can represent a geometric quantization parameter.

Illustratively, shift1 may be configured as 14.

The quantized geometric prediction residual information QtRes1 obtained by performing quantization processing may be:

res1 may represent geometric prediction residual information, and offset1 may represent half of the first predetermined geometric offset value, i.e. offset1 is 2 ^shift1-1 The rounding operation can be realized by offset 1.

In this embodiment, a first geometric quantization step size is determined according to a geometric quantization parameter, and the geometric prediction residual information is quantized based on the first geometric quantization step size and a first preset geometric offset value, so that a better quantization effect can be obtained.

Optionally, the geometric prediction residual information includes sub-geometric prediction residual information of three dimensions;

the geometric quantization parameter includes three sub-geometric quantization parameters respectively corresponding to the sub-geometric prediction residual information of the three dimensions.

Wherein, the three dimensions can be X, Y, Z dimensions in a three-dimensional coordinate system respectively. Three sub-geometric quantization parameters, which can respectively quantize the sub-geometric prediction residual information of three dimensions, can be configured by the parameter geompp [3] in the configuration file (cfg).

In this embodiment, the geometric quantization parameter includes three sub-geometric quantization parameters corresponding to the sub-geometric prediction residual information of the three dimensions, and the sub-geometric prediction residual information of the three dimensions can be quantized, so that robustness and adaptability of lossy quantization in a geometric information loop can be improved.

Optionally, the method further includes:

performing predictive coding on the attribute information of the point cloud to be coded to obtain attribute predictive residual information;

quantizing the attribute prediction residual error information according to the attribute quantization parameter to obtain quantized attribute prediction residual error information;

and entropy coding is carried out on the basis of the quantized attribute prediction residual error information to obtain an attribute code stream.

Wherein the first attribute quantization step size can be determined according to the attribute quantization parameter; and quantizing the attribute prediction residual information based on the first attribute quantization step size and a preset attribute offset value.

Wherein, the first attribute quantization step QS2 may be:

wherein QP2 may represent an attribute quantization parameter.

The quantization attribute prediction residual information QtRes2 obtained by performing quantization processing may be:

where Res2 may represent attribute prediction residual information, and offset2 may represent a preset attribute offset value, and offset2 may be set to 0.5, for example.

In addition, when predictive coding is performed, a prediction candidate list can be established for the attribute information of the point cloud to be coded, the best attribute predicted value is selected from the prediction candidate list, and the best attribute predicted value and the attribute information are subjected to subtraction to obtain attribute prediction residual information. Each attribute predictor in the prediction candidate list may correspond to an attribute prediction mode. For example, a prediction candidate list may be established in advance, and the prediction candidate list may include N attribute predictors, where the N attribute predictors correspond to the N attribute prediction modes in a one-to-one manner, and N is a positive integer greater than 1. For example, if the number of N is 4, that is, the prediction candidate list includes 4 attribute prediction values, and the point cloud to be encoded is the 5 th point cloud to be encoded in all the point clouds, the attribute prediction values may be determined by using the attribute information of the 4 point clouds to be encoded, which are located before the point cloud to be encoded and have an encoding sequence of 1 to 4. For example, the determination rule of the attribute predicted value may be that the first attribute predicted value is the sum of the attribute information of 4 point clouds to be encoded; the second attribute predicted value is the minimum attribute information of 4 point clouds to be coded; the third attribute predicted value is the average value of the attribute information of 4 point clouds to be coded; the fourth attribute predicted value is the difference value between the attribute information of the 4 th point cloud to be coded and the attribute information of the 3 rd point cloud to be coded. The attribute information of the point cloud to be encoded can be represented as three-dimensional coordinates (x, y, z) of the point cloud to be encoded.

It should be understood that the specific determination rule regarding the predicted value of the attribute may be flexibly set, and the embodiment is not specifically limited herein.

In the embodiment, the attribute information of the point cloud to be coded is subjected to predictive coding to obtain attribute prediction residual information; quantizing the attribute prediction residual error information according to the attribute quantization parameter to obtain quantized attribute prediction residual error information; and entropy coding is carried out on the basis of the quantized attribute prediction residual error information to obtain an attribute code stream. Therefore, quantization processing is introduced in the process of coding the attribute information, and the coding efficiency of the attribute information can be improved.

Optionally, the performing entropy coding based on the quantization attribute prediction residual information includes:

determining at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information;

obtaining rate distortion costs corresponding to the at least two candidate attribute prediction residual error information;

determining target quantized attribute prediction residual information according to rate distortion costs corresponding to the at least two candidate attribute prediction residual information;

entropy encoding is performed based on the target quantization attribute prediction residual information.

The at least two candidate attribute prediction residual information may include 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. For example, the at least two candidate attribute prediction residual information may comprise quantized attribute prediction residual information and a fixed value {0,0,0} when encoding color.

In the embodiment, the target quantization attribute prediction residual information is obtained by introducing a rate distortion optimization algorithm to the quantization attribute prediction residual information, and entropy coding is performed on the basis of the target quantization attribute prediction residual information, so that efficiency of lossy coding on the attribute information can be improved.

Optionally, the target quantized attribute prediction residual information is candidate attribute prediction residual information with the smallest rate distortion cost in the at least two candidate attribute prediction residual information.

The at least two candidate attribute prediction residual error information can be stored in a form of a candidate list, and the first candidate attribute prediction residual error information in the candidate list is used as the best candidate attribute prediction residual error information; traversing candidate attribute prediction residual error 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 optimal candidate attribute prediction residual information, updating the current candidate attribute prediction residual information into the optimal candidate attribute prediction residual information, otherwise, not updating the optimal candidate attribute prediction residual information; and after traversing the candidate list, determining the best candidate attribute prediction residual error information as target quantization attribute prediction residual error information. After determining the target quantized property prediction residual information, the target quantized property prediction residual information may be input to an encoder for entropy encoding.

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 that the lossy coding process of the attribute information can be optimized, and the point cloud coding efficiency is improved.

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 code 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 code rate is used to characterize a predicted bit value for encoding the candidate attribute prediction residual information.

The rate-distortion cost corresponding to the candidate attribute prediction residual information may be positively correlated to both the attribute distortion value and the second prediction residual code rate. For example, the rate-distortion cost2 corresponding to the candidate attribute prediction residual information may be:

cost2＝dist2+λ2*rate2

where λ 2 may represent a weight parameter of a code rate and distortion in a rate-distortion cost, for example, λ 2 may be set to 0.4,0.5 or 0.6, etc.; rate2 may represent a second prediction residual code rate; dist2 may represent an attribute distortion value. The calculation formula of the attribute distortion value dist2 can be as follows:

dist2＝normal1(recAttri-oriAttri)

the function normal1 represents that a norm of an expression is solved, recAttri represents a reconstruction attribute value obtained by using candidate attribute prediction residual information and an attribute prediction value, and oriAttri represents an original attribute value.

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 code rate, so that the rate-distortion cost corresponding to the candidate attribute prediction residual information can be determined more accurately.

Optionally, the at least two candidate attribute prediction residual information 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;

the entropy encoding based on the target quantization attribute prediction residual information comprises:

entropy encoding is performed based on the identifier corresponding to the target quantized geometric 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 related to the quantized attribute prediction residual information;

and if the target quantized attribute prediction residual information is candidate attribute prediction residual information irrelevant to the quantized attribute prediction residual information, performing entropy coding on the basis of an identifier corresponding to the target quantized attribute prediction residual information.

Wherein the candidate attribute prediction residual information is related to the quantized attribute prediction residual information, it may be that the candidate attribute prediction residual information may be obtained based on the quantized attribute prediction residual information, exemplarily, the candidate attribute prediction residual information is equal to the quantized attribute prediction residual information, or the candidate attribute prediction residual information is equal to an integer multiple of the quantized attribute prediction residual information, and so on; the candidate attribute prediction residual information is not related to the quantized attribute prediction residual information, and may be the preset attribute prediction residual information, for example, (0,0,0).

In addition, an attribute rate distortion optimization control parameter may be set, and if the attribute rate distortion optimization control parameter is a third preset value, the entropy encoding based on the target quantization attribute prediction residual information includes: entropy encoding is performed 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 related to the quantized attribute prediction residual information; and if the target quantized attribute prediction residual information is candidate attribute prediction residual information irrelevant to the quantized attribute prediction residual information, performing entropy coding on the basis of an identifier corresponding to the target quantized attribute prediction residual information.

If the attribute rate-distortion optimization control parameter is a fourth preset value, entropy encoding is performed on the target quantization attribute-based prediction residual information, and the method includes: entropy encoding based on the target quantized attribute prediction residual information, if the target quantized attribute prediction residual information is candidate attribute prediction residual information related to the quantized attribute prediction residual information; entropy encoding is performed based on the target quantized attribute prediction residual information, in a case where the target quantized attribute prediction residual information is candidate attribute prediction residual information irrelevant to the quantized attribute prediction residual information.

The third preset value and the fourth preset value are not limited in this embodiment. For example, the third preset value may be 1, and the fourth preset value may be 0.

Further, it may be determined that the target quantized property prediction residual information is related or not related to the quantized property prediction residual information by an identifier corresponding to the target quantized property prediction residual information. When the decoding end decodes, an identifier corresponding to the target quantized attribute prediction residual information may be firstly analyzed, and if it is determined that the target quantized attribute prediction residual information is not related to the quantized attribute prediction residual information according to the identifier corresponding to the target quantized attribute prediction residual information, the target quantized attribute prediction residual information may be found according to the identifier corresponding to the target quantized attribute prediction residual information; and if the correlation between the target quantized attribute prediction residual information and the quantized attribute prediction residual information is determined according to the identifier corresponding to the target quantized attribute prediction residual information, decoding from the attribute code stream to obtain the target quantized attribute prediction residual information.

In this embodiment, when the target quantized attribute prediction residual information is candidate attribute prediction residual information related to the quantized attribute prediction residual information, entropy encoding is performed based on an identifier corresponding to the target quantized attribute prediction residual information and the target quantized attribute prediction residual information; and if the target quantized attribute prediction residual information is candidate attribute prediction residual information irrelevant to the quantized attribute prediction residual information, performing entropy coding on the basis of an identifier corresponding to the target quantized attribute prediction residual information. In this way, the partial target quantized attribute prediction residual information may be encoded only with the flag corresponding to the target quantized attribute prediction residual information without being encoded, and the encoding efficiency can be further improved.

Referring to fig. 6, fig. 6 is a flowchart of a point cloud decoding processing method provided in an embodiment of the present application, and as shown in fig. 6, the point cloud decoding processing method includes the following steps:

step 201, entropy decoding is carried out on the geometric code stream to obtain quantized geometric prediction residual error information;

202, carrying out inverse quantization processing on the quantized geometric prediction residual error information according to geometric quantization parameters to obtain geometric prediction residual error information;

and 203, performing predictive decoding on the basis of the geometric predictive residual error information to obtain geometric information of the point cloud to be decoded.

The geometric code stream can be entropy decoded to obtain quantized geometric prediction residual information and a geometric prediction mode. And performing predictive decoding based on the geometric prediction residual error information and the geometric prediction mode to obtain the geometric information of the point cloud to be decoded. Illustratively, the geometric prediction mode can be analyzed, and a corresponding geometric prediction value is selected according to the geometric prediction mode; and adding the geometric predicted value and the geometric prediction residual error information to obtain the geometric information of the point cloud to be decoded. The geometric information may include geometric coordinates.

Optionally, the entropy decoding the geometric code stream to obtain quantized geometric prediction residual information includes:

and under the condition that the geometric quantization control parameter indicates that quantization processing is started, entropy decoding is carried out on the geometric code stream to obtain quantized geometric prediction residual error information.

and under the condition that the geometric quantization control parameter indicates that quantization processing is not started, entropy decoding is carried out on the geometric code stream to obtain geometric prediction residual error information.

Optionally, the inverse quantization processing on the quantized geometric prediction residual information according to the geometric quantization parameter includes:

determining a second geometric quantization step size according to the geometric quantization parameter;

and performing inverse quantization processing on the quantized geometric prediction residual error information based on the second geometric quantization step size and a second preset geometric offset value.

Wherein, the second geometric quantization step QS3 may be:

therein, 2 ^shift3 A second preset geometric deviation value may be represented, shift3 may represent the number of bits deviated in the quantization processing process, the larger shift3 represents the more accurate the quantization result, and QP1 may represent a geometric quantization parameter.

Illustratively, shift3 may be configured as 6.

The geometric prediction residual information RQtRes1 obtained by performing inverse quantization processing may be:

wherein, qtRes1 may represent quantized geometric prediction residual information, and offset3 may represent half of the second predetermined geometric offset value, i.e. offset3 is 2 ^shift3-1 。

Optionally, the method further includes:

entropy decoding is carried out on the attribute code stream to obtain quantized attribute prediction residual error information;

performing inverse quantization processing on the quantized attribute prediction residual information according to the attribute quantization parameter to obtain attribute prediction residual information;

and performing predictive decoding based on the attribute prediction residual error information to obtain the attribute information of the point cloud to be decoded.

The entropy decoding of the attribute code stream to obtain quantized attribute prediction residual information may include: determining whether the attribute quantization control parameter indicates that quantization processing is enabled; under the condition that the attribute quantization control parameter indicates that quantization processing is started, entropy decoding is carried out on the attribute code stream to obtain quantized attribute prediction residual error information; and under the condition that the attribute quantization control parameter indicates that quantization processing is not started, entropy decoding is carried out on the attribute code stream to obtain attribute prediction residual error information.

The attribute code stream can be entropy decoded to obtain quantized attribute prediction residual information and an attribute prediction mode. And performing predictive decoding based on the attribute prediction residual error information and the attribute prediction mode to obtain the attribute information of the point cloud to be decoded. Illustratively, the attribute prediction mode can be analyzed, and a corresponding attribute prediction value is selected according to the attribute prediction mode; and adding the attribute predicted value and the attribute prediction residual information to obtain the attribute information of the point cloud to be decoded. The attribute information may include attribute coordinates.

Wherein, the second attribute quantization step may be determined according to the attribute quantization parameter, and the second attribute quantization step QS4 may be:

wherein QP2 may represent an attribute quantization parameter.

The attribute prediction residual information RQtRes2 obtained by performing inverse quantization processing may be:

RQtRes2＝QtRes2·QS4

where, qtRes2 may represent quantization attribute prediction residual information.

It should be noted that, this embodiment is used as an implementation of the decoding side corresponding to the embodiment shown in fig. 3, and specific implementations thereof may refer to relevant descriptions of the embodiment shown in fig. 3, so that, in order to avoid repeated descriptions, this embodiment is not described again, and the same beneficial effects may also be achieved.

It should be noted that, in the point cloud encoding processing method provided in the embodiment of the present application, the execution subject may be a point cloud encoding processing apparatus, or a control module of the point cloud encoding processing apparatus for executing the method of point cloud encoding processing. The embodiment of the present application describes a point cloud encoding processing apparatus provided in the embodiment of the present application, by taking a method for executing point cloud encoding processing by a point cloud encoding processing apparatus as an example.

Referring to fig. 7, fig. 7 is a structural diagram of a point cloud encoding processing apparatus according to an embodiment of the present disclosure, and as shown in fig. 7, the point cloud encoding processing apparatus 300 includes:

the first encoding module 301 is configured to perform predictive encoding based on geometric information of a point cloud to be encoded to obtain geometric prediction residual information;

a first quantization module 302, configured to perform quantization processing on the geometric prediction residual information according to a geometric quantization parameter to obtain quantized geometric prediction residual information;

and a second encoding module 303, configured to perform entropy encoding based on the quantized geometric prediction residual information to obtain a geometric code stream.

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

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

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

Optionally, as shown in fig. 8, the second encoding module 303 specifically includes:

a first determining unit 3031 configured to determine at least two candidate geometric prediction residual information based on the quantized geometric prediction residual information;

a first obtaining unit 3032, configured to obtain rate-distortion costs corresponding to the at least two candidate geometric prediction residual information;

a second determining unit 3033, configured to determine target quantized geometric prediction residual information according to rate-distortion costs corresponding to the at least two candidate geometric prediction residual information;

a first encoding unit 3034, configured to perform entropy encoding based on the target quantized geometric prediction residual information.

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

the first encoding unit 3034 is specifically configured to:

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 related to the quantized geometric prediction residual information;

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

carrying out duplicate removal processing on the quantized point cloud;

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

Optionally, as shown in fig. 9, the apparatus 300 further includes:

a third encoding module 304, configured to perform predictive encoding on the attribute information of the point cloud to be encoded, to obtain attribute predictive residual information;

a second quantization module 305, configured to perform quantization processing on the attribute prediction residual information according to the attribute quantization parameter to obtain quantized attribute prediction residual information;

and a fourth encoding module 306, configured to perform entropy encoding on the basis of the quantized attribute prediction residual information to obtain an attribute code stream.

Optionally, as shown in fig. 10, the fourth encoding module 306 specifically includes:

a third determining unit 3061 for determining at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information;

a second obtaining unit 3062, configured to obtain rate-distortion costs corresponding to the at least two candidate attribute prediction residual information;

a fourth determining unit 3063, configured to determine target quantized attribute prediction residual information according to rate distortion costs corresponding to the at least two candidate attribute prediction residual information;

a second encoding unit 3064 for entropy encoding based on the target quantization property prediction residual information.

the second encoding unit 3064 is specifically configured to:

The point cloud encoding processing device 300 in the embodiment of the present application can improve the rate control effect of the geometric code stream of the point cloud.

The point cloud encoding processing device in the embodiment of the present application may be a device, a device with an operating system, or an electronic device, and may also 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 above listed types of terminals, and the non-mobile terminal may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine, a kiosk, or the like, and the embodiments of the present application are not limited in particular.

The point cloud encoding processing apparatus 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 for avoiding repetition, details are not repeated here.

It should be noted that, in the point cloud decoding processing method provided in the embodiment of the present application, the execution subject may be a point cloud decoding processing apparatus, or a control module in the point cloud decoding processing apparatus for executing the method of point cloud decoding processing. The point cloud decoding processing device provided by the embodiment of the present application is described by taking a method for executing point cloud decoding processing by a point cloud decoding processing device as an example.

Referring to fig. 11, fig. 11 is a structural diagram of a point cloud decoding processing apparatus according to an embodiment of the present disclosure, and as shown in fig. 11, the point cloud decoding processing apparatus 400 includes:

a first decoding module 401, configured to perform entropy decoding on the geometric code stream to obtain quantized geometric prediction residual information;

a first inverse quantization module 402, configured to perform inverse quantization processing on the quantized geometric prediction residual information according to a geometric quantization parameter to obtain geometric prediction residual information;

and a second decoding module 403, configured to perform predictive decoding based on the geometric prediction residual information to obtain geometric information of the point cloud to be decoded.

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

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

Optionally, the first inverse quantization module 402 is specifically configured to:

Optionally, as shown in fig. 12, the apparatus 400 further includes:

a third decoding module 404, configured to perform entropy decoding on the attribute code stream to obtain quantized attribute prediction residual information;

a second inverse quantization module 405, configured to perform inverse quantization processing on the quantized attribute prediction residual information according to the attribute quantization parameter, so as to obtain attribute prediction residual information;

and a fourth decoding module 406, configured to perform predictive decoding based on the attribute prediction residual information to obtain attribute information of the point cloud to be decoded.

The point cloud decoding processing device 400 in the embodiment of the present application can improve the rate control effect of the geometric code stream of the point cloud.

The point cloud decoding processing device in the embodiment of the present application may be a device, a device with an operating system, or an electronic device, and may also 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 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 (teller machine), a self-service machine (kiosk), or the like, and the embodiments of the present application are not limited in particular.

The point cloud decoding processing apparatus provided in the embodiment of the present application can implement each process implemented in the method embodiment of fig. 6, and achieve the same technical effect, and for avoiding repetition, details are not repeated here.

Optionally, as shown in fig. 13, 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 each process of the above-mentioned point cloud encoding processing method embodiment, and can achieve the same technical effect; alternatively, when being executed by the processor 501, the program or the instruction realizes each process of the above-described point cloud decoding processing method embodiment, and can achieve the same technical effect, and is not described herein again to avoid repetition.

The embodiment of the present application further provides a terminal, which includes a processor and a communication interface, where the embodiment of the terminal corresponds to the embodiment of the point cloud encoding processing method, or the embodiment of the terminal corresponds to the embodiment of the point cloud decoding processing method, and all implementation processes and implementation manners of the embodiment of the method are applicable to the embodiment of the terminal, and can achieve the same technical effect. Specifically, fig. 14 is a schematic diagram of a hardware structure of a terminal for implementing the embodiment of the present application.

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

Those skilled in the art will appreciate that the terminal 600 may further include a power supply (e.g., a battery) for supplying power to various components, and the power supply may be logically connected to the processor 610 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The terminal structure shown in fig. 6 does not constitute a limitation of the terminal, and the terminal may include more or less components than those shown, or may combine some components, or may be arranged differently, and thus, the description thereof is omitted.

It is to be understood that, in the embodiment of the present application, the input Unit 604 may include a Graphics Processing Unit (GPU) 6041 and a microphone 6042, and the Graphics Processing Unit 6041 processes image data of a still picture or a video obtained by an image capturing apparatus (such as a camera) in a video capturing mode or an image capturing 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, or the like. The user input unit 607 includes a touch panel 6071 and other input devices 6072. A touch panel 6071, also referred to as a touch screen. The touch panel 6071 may include two parts of a touch detection device and a touch controller. Other input devices 6072 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 the embodiment of the present application, the radio frequency unit 601 receives downlink data from a network side device and then processes the downlink data in the processor 610; in addition, the uplink data is sent to the network side equipment. In general, 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, and the like.

The memory 609 may be used to store software programs or instructions as well as various data. The memory 609 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, etc.) required for at least one function, and the like. Further, the Memory 609 may include a high-speed random access Memory, and may further include a nonvolatile Memory, wherein 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 610 may include one or more processing units; alternatively, the processor 610 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 the processor 610.

Wherein, the terminal is used for executing the point cloud coding processing method, and comprises the following steps:

the processor or the communication interface is to: performing predictive coding based on the geometric information of the point cloud to be coded to obtain geometric predictive residual information; carrying out quantization processing on the geometric prediction residual error information according to the geometric quantization parameter to obtain quantized geometric prediction residual error information; and performing entropy coding on the basis of the quantized geometric prediction residual error information to obtain a geometric code stream.

Optionally, the processor 610 is further configured to:

determining target quantized geometric prediction residual error information according to the rate distortion cost corresponding to the at least two candidate geometric prediction residual error information;

Optionally, the at least two candidate geometric prediction residual information 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;

the processor 610 is further configured to:

Optionally, the processor 610 is further configured to:

carrying out duplicate removal processing on the quantized point cloud;

Optionally, the processor 610 is further configured to:

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

Optionally, the related candidate attribute prediction residual information and the candidate attribute prediction residual information irrelevant to the quantized attribute prediction residual information;

the processor 610 is further configured to:

entropy encoding is performed on the basis of the identifier corresponding to the target quantized geometric 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 related to the quantized attribute prediction residual information;

The terminal in the embodiment of the application can improve the rate control effect of the geometric code stream of the point cloud.

Specifically, the terminal of the embodiment of the present application further includes: the instructions or programs stored in the memory 609 and capable of being executed on the processor 610, the processor 610 calls the instructions or programs in the memory 609 to execute the methods executed by the modules shown in fig. 7, and achieve the same technical effect, and are not described herein in detail to avoid repetition.

Wherein, the terminal is used for executing the point cloud decoding processing method, and comprises the following steps:

the processor or the communication interface is to: entropy decoding is carried out on the geometric code stream to obtain quantized geometric prediction residual information; carrying out inverse quantization processing on the quantized geometric prediction residual error information according to geometric quantization parameters to obtain geometric prediction residual error information; and performing predictive decoding based on the geometric prediction residual error information to obtain the geometric information of the point cloud to be decoded.

Optionally, the processor 610 is further configured to:

Specifically, the terminal of the embodiment of the present application further includes: the instructions or programs stored in the memory 609 and capable of being executed on the processor 610, the processor 610 calls the instructions or programs in the memory 609 to execute the method executed by each module shown in fig. 11, and achieve the same technical effect, and are not described herein in detail to avoid repetition.

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 the program or the instruction, when executed by the processor, implements each process of the above-mentioned point cloud encoding processing method embodiment, or the program or the instruction, when executed by the processor, implements each process of the above-mentioned point cloud decoding processing method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the description is omitted 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 processing method embodiment, or to implement each process of the above-mentioned point cloud decoding processing 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 apparatus of 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 involved, e.g., the methods described may be performed in an order different than 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 processing method is characterized by comprising the following steps:

2. The method according to claim 1, wherein the quantizing the geometric prediction residual information according to a geometric quantization parameter comprises:

3. The method of claim 2, wherein after determining whether the geometric quantization control parameter indicates that quantization processing is enabled, the method further comprises:

4. The method according to claim 1, wherein the predictive coding of the geometric information of the point cloud to be coded comprises:

5. The method of claim 1, wherein entropy encoding based on the quantized geometric prediction residual information comprises:

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

7. The method of claim 5, wherein a rate distortion cost corresponding to the candidate geometric prediction residual information is determined based on a geometric distortion value used for characterizing a geometric distortion corresponding to the candidate geometric prediction residual information and a first prediction residual code rate used for characterizing a bit value for encoding a prediction of the candidate geometric prediction residual information.

8. The method according to claim 5, wherein the at least two candidate geometric prediction residual information comprise 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;

9. The method according to claim 1, wherein the predictive coding of the geometric information of the point cloud to be coded comprises:

carrying out duplicate removal processing on the quantized point cloud;

10. The method according to claim 1, wherein the quantizing the geometric prediction residual information according to a geometric quantization parameter comprises:

11. The method of claim 1, wherein the geometric prediction residual information comprises three-dimensional sub-geometric prediction residual information;

12. The method of claim 1, further comprising:

13. The method of claim 12, wherein entropy encoding the prediction residual information based on the quantization attribute comprises:

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

15. The method of claim 13, wherein a rate-distortion cost associated with the candidate attribute prediction residual information is determined based on an attribute distortion value used to characterize an attribute distortion associated with the candidate attribute prediction residual information and a second prediction residual code rate used to characterize a bit value predicted by encoding the candidate attribute prediction residual information.

16. The method of claim 13, wherein the at least two candidate attribute prediction residual information comprise candidate attribute prediction residual information related to the quantization attribute prediction residual information and candidate attribute prediction residual information not related to the quantization attribute prediction residual information;

17. A point cloud decoding processing method is characterized by comprising the following steps:

18. The method according to claim 17, wherein said entropy decoding the geometric code stream to obtain quantized geometric prediction residual information comprises:

19. The method of claim 18, wherein after determining whether the geometric quantization control parameter indicates that quantization processing is enabled, the method further comprises:

20. The method according to claim 17, wherein said inverse quantizing said quantized geometric prediction residual information according to a geometric quantization parameter comprises:

21. The method of claim 17, wherein the geometric prediction residual information comprises three-dimensional sub-geometric prediction residual information;

22. The method of claim 17, further comprising:

23. A point cloud encoding processing apparatus, comprising:

the first coding module is used for carrying out predictive coding on the basis of geometric information of point cloud to be coded to obtain geometric prediction residual information;

24. The apparatus of claim 23, wherein the first quantization module is specifically configured to:

25. The apparatus of claim 23, wherein the second encoding module specifically comprises:

a first determining unit for determining at least two candidate geometric prediction residual information based on the quantized geometric prediction residual information;

a first obtaining unit, configured to obtain rate distortion costs corresponding to the at least two candidate geometric prediction residual information;

a second determining unit, configured to determine target quantized geometric prediction residual information according to rate-distortion costs corresponding to the at least two candidate geometric prediction residual information;

a first encoding unit for entropy encoding based on the target quantized geometric prediction residual information.

26. The apparatus of claim 23, further comprising:

the third coding module is used for carrying out predictive coding on the attribute information of the point cloud to be coded to obtain attribute prediction residual error information;

the second quantization module is used for performing quantization processing on the attribute prediction residual error information according to the attribute quantization parameter to obtain quantized attribute prediction residual error information;

and the fourth coding module is used for entropy coding based on the quantized attribute prediction residual error information to obtain an attribute code stream.

27. The apparatus of claim 26, wherein the fourth encoding module specifically comprises:

a third determining unit for determining at least two candidate attribute prediction residual information based on the quantized attribute prediction residual information;

a second obtaining unit, configured to obtain rate distortion costs corresponding to the at least two candidate attribute prediction residual information;

a fourth determining unit, configured to determine target quantized attribute prediction residual information according to rate distortion costs corresponding to the at least two candidate attribute prediction residual information;

a second encoding unit for entropy encoding based on the target quantization attribute prediction residual information.

28. A point cloud decoding processing apparatus, comprising:

29. 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 processing method of any of claims 1 to 16; alternatively, the program or instructions, when executed by the processor, implement the steps of the point cloud decoding processing method of any of claims 17 to 22.

30. 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 processing method of any one of claims 1 to 16, or which, when executed by a processor, implement the steps of the point cloud decoding processing method of any one of claims 17 to 22.