CN116094694A - Point cloud geometric coding method, decoding method, coding device and decoding device - Google Patents
Point cloud geometric coding method, decoding method, coding device and decoding device Download PDFInfo
- Publication number
- CN116094694A CN116094694A CN202211604777.XA CN202211604777A CN116094694A CN 116094694 A CN116094694 A CN 116094694A CN 202211604777 A CN202211604777 A CN 202211604777A CN 116094694 A CN116094694 A CN 116094694A
- Authority
- CN
- China
- Prior art keywords
- node
- context
- current sub
- occupation
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/0819—Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s)
- H04L9/083—Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s) involving central third party, e.g. key distribution center [KDC] or trusted third party [TTP]
- H04L9/0833—Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s) involving central third party, e.g. key distribution center [KDC] or trusted third party [TTP] involving conference or group key
- H04L9/0836—Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s) involving central third party, e.g. key distribution center [KDC] or trusted third party [TTP] involving conference or group key using tree structure or hierarchical structure
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/08—Protocols specially adapted for terminal emulation, e.g. Telnet
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/0852—Quantum cryptography
- H04L9/0858—Details about key distillation or coding, e.g. reconciliation, error correction, privacy amplification, polarisation coding or phase coding
Abstract
The invention discloses a point cloud geometric coding method, a decoding method, coding equipment and decoding equipment. The point cloud is defined in a tree structure, each node in the tree structure comprises a plurality of sub-nodes, and the point cloud geometric decoding method comprises the following steps: determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node; and performing entropy decoding according to the upper and lower Wen Duidian cloud geometric code streams of the current child node to obtain the occupation information of the current child node. The invention determines the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node, considers the occupation number of the adjacent nodes of the multi-class adjacent relation of the current sub-node, determines the context according to the geometric structure of the adjacent nodes based on the combined occupation number, better utilizes the spatial distribution correlation of the adjacent nodes and improves the geometric compression performance of the point cloud.
Description
Technical Field
The invention relates to the technical field of point cloud processing, in particular to a point cloud geometric coding method, a decoding method, coding equipment and decoding equipment based on octree.
Background
Point cloud compression is largely divided into geometric compression and attribute compression, and encoders in the geometric compression framework described in TMC13v12 (Test Model for Category 1&3version 12), a test platform currently provided by International standards organization (Moving Picture Experts Group, MPEG), progressively divide the point cloud (after alignment with bounding boxes) into eight sub-nodes. Only non-empty sub-pixels continue to be subdivided. According to the data structure characteristics of the octree, the position of each voxel is represented by its cell center.
Meanwhile, the point cloud geometric compression method described in the test platform PCRM v9.0 provided by the Chinese AVS (Audio Video coding Standard) point cloud compression working group is mainly adopted to construct a mixed traversal octree, generate space occupation codes and encode the occupation codes through context.
Based on the geometry coding of the octree, each time an octree node is divided, the space occupying code of the node contains eight bits (b 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 ) Each representing the occupation of eight child nodes of the node. The method comprises the steps of (1) performing entropy coding on each sub-node occupied bit code by using a separate context, adopting a context adaptive entropy coder (CABAC) widely applied in the AVS2 standard, and coding each bit (bit or bin) of the space occupied code so as to achieve a better compression effect, wherein the coding is mainly divided into two parts, and (1) selecting the context; (2) binary arithmetic coding.
The octree geometric coding in the existing AVS point cloud coding standard firstly considers the occupied number C1 of 3 coplanar adjacent nodes of the current sub-node and then considers the occupied number C2 of 3 collinear adjacent nodes. The occupied numbers are only the number of coplanar adjacent nodes and the number of collinear adjacent nodes, the geometric structures of the coplanar and collinear adjacent nodes are not considered, and the space correlation of the adjacent nodes cannot be well utilized, so that proper probability is not selected, and the compression performance is reduced.
Accordingly, the prior art is still in need of improvement and development.
Disclosure of Invention
The invention provides a point cloud geometric coding method, a decoding method, coding equipment and decoding equipment, which are used for determining the context of a current sub-node according to the combination occupation quantity of adjacent nodes of the current sub-node of an octree by considering the geometric structures of the adjacent nodes.
The technical scheme adopted by the invention is as follows:
a method for geometric decoding of a point cloud, the point cloud being defined in a tree structure, each node in the tree structure comprising a plurality of child nodes, comprising the steps of:
determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node;
and performing entropy decoding according to the upper and lower Wen Duidian cloud geometric code streams of the current child node to obtain the occupation information of the current child node.
The method for decoding the point cloud geometry is characterized in that the determining the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node comprises the following steps:
determining the context of the current sub-node according to the combination occupation number of the coplanar collinear adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes;
or determining the context of the current sub-node according to the combined occupation number of the co-planar co-linear co-point adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes.
The method for decoding the point cloud geometry is characterized in that the determining the context of the current sub-node according to the geometry of the neighboring nodes according to the combined occupation number of the neighboring nodes of the current sub-node comprises the following steps:
determining the context of the current sub-node according to the combination occupation number of the adjacent nodes of the current sub-node and all the geometric structure types of the adjacent nodes under the combination occupation number;
or according to the combination occupation number of the adjacent nodes of the current sub-node, according to all the geometric structure types of the adjacent nodes under the combination occupation number, and according to the coplanar adjacent node number, carrying out geometric structure type reduction of the adjacent nodes, and determining the context of the current sub-node.
The point cloud geometric decoding method is characterized by comprising the following steps of:
determining the context of the neighboring node based on the child node according to the combined occupation number of the neighboring nodes of the current child node;
determining the context of the adjacent node based on the node where the child node is located according to the occupation condition of the adjacent node of the node where the current child node is located;
determining the neighboring node occupation context of the current child node according to the context of the neighboring node based on the child node and the context of the neighboring node based on the node where the child node is located;
and determining the context of the current sub-node according to the occupation context of the neighbor node of the current sub-node.
The method for decoding the point cloud geometry is characterized in that the determining the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node comprises the following steps:
determining the occupation context of the neighbor nodes of the current sub-node according to the combined occupation number of the neighbor nodes of the current sub-node;
determining the occupation context of the neighboring sub-node of the current sub-node according to the occupation information of the coded neighboring sub-node of the current sub-node;
and determining the context of the current sub-node according to the neighboring node occupation context of the current sub-node and the neighboring sub-node occupation context.
The point cloud geometric decoding method is characterized in that the determining the occupation context of the neighboring sub-node of the current sub-node according to the occupation information of the encoded neighboring sub-node of the current sub-node, including:
obtaining an information state M according to the occupation information of the coded adjacent child node of the current child node;
and converting the state M through a sliding window to obtain a state N, and taking the state N as the neighboring child node occupation context of the current child node.
A point cloud geometric decoding device, which is characterized by comprising a processor, a memory and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps in the point cloud geometry decoding method as described in any one of the above.
A method of geometric encoding a point cloud, the point cloud being defined in a tree structure, each node in the tree structure comprising a plurality of child nodes, comprising the steps of:
determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node;
and carrying out entropy coding on the occupation information of the current sub-node according to the context of the current sub-node to obtain a point cloud geometric code stream.
The method for point cloud geometric coding is characterized in that the determining the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node comprises the following steps:
determining the context of the current sub-node according to the combination occupation number of the coplanar collinear adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes;
or determining the context of the current sub-node according to the combined occupation number of the co-planar co-linear co-point adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes.
The point cloud geometric coding device is characterized by comprising a processor, a memory and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps in the point cloud geometric encoding method as described in any one of the above.
Compared with the prior art, the point cloud geometric coding method, the decoding method, the coding equipment and the decoding equipment provided by the invention have the following beneficial effects. The invention determines the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node, wherein the combined occupation number considers the occupation number of the neighboring nodes of the multi-class neighboring relationship of the current sub-node, and determines the context according to the geometry structure of the neighboring nodes based on the combined occupation number. Compared with the prior art which only considers the occupation number of single adjacent relations, the invention considers the combined occupation number of adjacent nodes of multiple adjacent relations and the geometric structures of the adjacent nodes under different numbers, and the space distribution correlation of the adjacent nodes is better utilized according to the combined occupation number and the context of geometric structure design, so that the geometric compression performance of the point cloud is improved.
Drawings
FIG. 1 is a schematic flow chart of a point cloud geometry decoding method according to an embodiment of the invention;
FIG. 2 is another schematic flow chart diagram of a point cloud geometry decoding method according to an embodiment of the invention;
FIG. 3 is another schematic flow chart diagram of a point cloud geometry decoding method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a geometry of a current sub-node with a combined occupation number of 1;
FIG. 5 is another schematic diagram of a geometry of a current node with a combined occupation number of 1;
FIG. 6 is a schematic diagram of a geometry of a current sub-node with a combined occupation number of 2;
FIG. 7 is a schematic diagram of a geometry of a current child node with a combined occupation number of 3;
FIG. 8 is a schematic diagram illustrating the geometrical species reduction of neighboring nodes of a current child node according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of an occupancy of a neighboring node of a node where a current child node is located according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of occupancy of a coded neighboring child node of a current child node according to an embodiment of the present invention;
FIG. 11 is another schematic diagram illustrating occupancy of a coded neighboring child node of a current child node according to an embodiment of the present invention;
FIG. 12 is a schematic flow chart diagram of a point cloud geometry encoding method according to an embodiment of the present invention;
fig. 13 is a block diagram of the apparatus structure of the embodiment of the present invention.
Detailed Description
The invention provides a point cloud geometric coding method, a decoding method, coding equipment and decoding equipment, which are used for making the purposes, technical schemes and effects of the invention clearer and more definite, and the invention is further described in detail below by referring to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention is further illustrated by the following description of specific embodiments, taken in conjunction with the accompanying drawings.
The specific use scenario of the invention is octree-based point cloud geometric coding and geometric decoding.
Referring to fig. 1, the present invention provides a point cloud geometric decoding method, wherein the point cloud is defined in a tree structure, and each node in the tree structure includes a plurality of child nodes, and the method includes the following steps:
d1: determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node;
d2: and performing entropy decoding according to the upper and lower Wen Duidian cloud geometric code streams of the current child node to obtain the occupation information of the current child node.
In this embodiment, the steps D1-D2 are executed at the decoding end, so as to implement the geometric decoding process of the point cloud. According to the octree structure, each node in the tree structure comprises 8 sub-nodes, and the occupation code corresponding to the 8 sub-nodes is (b) 7 b 6 b 5 b 4 b 3 b 2 b 1 b 0 ) Each child node corresponds to one occupancy information b k K=0, 1, … 7. B if the child node does not contain any points k =0; conversely, b k ＝1。
Here, the context of the current sub-node is determined according to the combined occupation number of the neighboring nodes of the current sub-node, and then entropy decoding is performed according to the upper and lower Wen Duidian cloud geometric code streams of the current sub-node, so as to obtain occupation information b of the current sub-node k 。
Here, the neighboring node of the current sub-node refers to a node that is coplanar, collinear, or co-located with the current sub-node.
In one possible implementation manner, the determining the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node includes:
d11: determining the context of the current sub-node according to the combination occupation number of the coplanar collinear adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes;
d12: or determining the context of the current sub-node according to the combined occupation number of the co-planar co-linear co-point adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes.
Here, the combined occupation number refers to the combined occupation number of at least 2 kinds of adjacent nodes in the adjacent relationship including the coplanarity, or collineation, or co-point of the current sub-node.
Fig. 4 is a schematic diagram of a geometry of a current node with a combined occupation number of 1. And combining the occupied numbers of the coplanar and collinear adjacent nodes of the current child node, and when the combined occupied number is 1, distributing 2 contexts when 2 cases exist in the geometric structures of the adjacent nodes. Fig. 4a shows that there are 1 co-planar neighbors for a neighbor node and fig. 4b shows that there are 1 co-planar neighbors for a neighbor node. Wherein a is the current sub-node, A is the node where the current sub-node is located, B is the coplanar adjacent node of the current sub-node, and C is the collinear adjacent node of the current sub-node.
Fig. 5 is another schematic diagram of a geometry of a current node of the present invention when the number of combined occupation of neighboring nodes is 1. And combining the occupied numbers of the coplanar collinear common-point adjacent nodes of the current child node, and when the combined occupied number is 1, distributing 3 contexts when the geometric structure of the adjacent nodes has 3 conditions. Fig. 5a shows that the neighboring node has 1 coplanar neighboring node, fig. 5b shows that the neighboring node has 1 collinear neighboring node, and fig. 5c shows that the neighboring node has 1 co-point neighboring node. Wherein a is the current sub-node, A is the node where the current sub-node is located, B is the coplanar adjacent node of the current sub-node, C is the collinear adjacent node of the current sub-node, and D is the co-point adjacent node of the current sub-node.
In one possible implementation manner, the determining the context of the current sub-node according to the geometry of the neighboring nodes according to the combined occupation number of the neighboring nodes of the current sub-node includes:
determining the context of the current sub-node according to the combination occupation number of the adjacent nodes of the current sub-node and all the geometric structure types of the adjacent nodes under the combination occupation number;
or determining all geometric structure types of the adjacent nodes according to the combination occupation number of the adjacent nodes of the current sub-node, and reducing the geometric structure types of the adjacent nodes according to the coplanar adjacent node number to determine the context of the current sub-node.
By way of example, the context of a current child node is determined from the combined occupancy number of co-planar co-linear neighbors of the current child node, according to all the geometry categories of the neighbors under the combined occupancy number. When the number of the combination occupation is 0, the geometry of the adjacent nodes is only 1, namely, no coplanar and collinear adjacent nodes exist, and 1 context is distributed; when the number of combinations occupies 1, the geometry of the adjacent nodes is 2, namely 1 coplanar and 1 collinear, and 2 contexts are allocated as shown in fig. 4 a.
Fig. 6 is a schematic diagram of a geometry of a current child node with a combined occupation number of 2. When the number of the combination occupation is 2, the geometry of the adjacent nodes is 4, namely 2 coplanar 1, as shown in fig. 6 a; 2 are collinear 1, as shown in fig. 6 b; 1 are coplanar and 1 are collinear for 2 species as shown in figures 6c and 6 d.
Fig. 7 is a schematic diagram of a geometry of a current child node with a combined occupation number of 3. When the number of the combination occupation is 3, the geometry of the adjacent nodes is 6, namely 3 coplanar 1, as shown in fig. 7 a; 3 co-linear 1 species as shown in fig. 7 d; 2 are coplanar 1 and collinear 2, as shown in figures 7b and 7 e; 1 are co-planar and 2 co-linear, 2, as shown in figures 7c, 7 f. Considering symmetry of occupied nodes and unoccupied nodes, the combined occupied number is 4,5,6 and occupied number is 2,1, 0. The combined occupancy number, geometry, and context of neighboring nodes to the current child node are shown in table 1.
Table 1 combined occupancy number, geometry, and context of neighboring nodes to the current child node
The number of occupied neighboring nodes of the current child node ranges from 0 to 6, the context determined according to the geometry of the neighboring nodes is denoted by C1, the number of C1 is 1+2+4+6+4+2+1=20, and the range of C1 is 0 to 19.
By way of example, all geometric categories of the neighboring nodes are determined according to the combined occupation number of the neighboring nodes of the current sub-node, and the geometric categories of the neighboring nodes are reduced according to the coplanar neighboring node number, so that the context of the current sub-node is determined. And (3) reducing the types of the geometric structures of the adjacent nodes according to the number of the coplanar adjacent nodes, so that the number of corresponding contexts can be reduced, and the corresponding statistical probabilities can be combined. The statistical probabilities are reasonably combined, and the entropy coding efficiency can be more efficient.
Fig. 8 is a schematic diagram illustrating geometric type reduction of neighboring nodes of a current child node according to an embodiment of the present invention. Consider 3 geometries of neighboring nodes that are not coplanar as 1 downscaling, i.e., 3 geometries of neighboring nodes that are 1 collinear, 2 collinear, and 3 collinear as 1 downscaling, as shown in fig. 8 a; the 3 geometries including 3 coplanar neighbors are considered 1 downscaling, i.e., the 3 geometries of 3 coplanar 1 collinear, 3 coplanar 2 collinear, and 3 coplanar 3 collinear neighbors are considered 1 downscaling, as shown in fig. 8 b. Thus, after the reduction, the original 20 geometric structures and the corresponding contexts are reduced to 16 types, and the range of values is 0-15.
Fig. 2 is another schematic flow chart of a point cloud geometry decoding method according to an embodiment of the invention. In one possible implementation, determining the context of the current child node according to the combined occupancy number of neighboring nodes of the current child node includes:
d1': determining the context of the neighboring node based on the child node according to the combined occupation number of the neighboring nodes of the current child node;
d2': determining the context of the adjacent node based on the node where the child node is located according to the occupation condition of the adjacent node of the node where the current child node is located;
d3': determining the neighboring node occupation context of the current child node according to the context of the neighboring node based on the child node and the context of the neighboring node based on the node where the child node is located;
d4': and determining the context of the current sub-node according to the occupation context of the neighbor node of the current sub-node.
Here, the context C1 based on the neighboring node of the current sub-node is determined according to the combined occupation number of the neighboring nodes of the current sub-node, the context C2 based on the neighboring node of the current sub-node is determined according to the occupation situation of the neighboring node of the current sub-node, the neighboring node occupation context c= (C1) ×2+c2 of the current sub-node is determined according to C1 and C2, and the context of the current sub-node is determined according to C.
Fig. 9 is a schematic diagram of occupancy of a neighboring node of a node where a current child node is located according to an embodiment of the present invention. And distributing 6 coplanar adjacent nodes of the node where the current child node is located in the 3 axial directions to serve as 1 geometric structure, distributing 1 context 0, and distributing 6 coplanar adjacent nodes of the node where the current child node is located in the 3 axial directions to serve as 1 geometric structure, and distributing 1 context 1.
Fig. 3 is another schematic flow chart of a point cloud geometry decoding method according to an embodiment of the invention. In one possible implementation, determining the context of the current child node according to the combined occupancy number of neighboring nodes of the current child node includes:
d1', determining the occupation context of the neighbor node of the current sub-node according to the combined occupation number of the neighbor nodes of the current sub-node;
d2', determining the occupation context of the neighboring sub-node of the current sub-node according to the occupation information of the coded neighboring sub-node of the current sub-node;
and D3' determining the context of the current sub-node according to the neighboring node occupation context and the neighboring sub-node occupation context of the current sub-node.
Here, determining a neighboring node occupation context C of a current sub-node according to the combined occupation number of neighboring nodes of the current sub-node; determining a neighboring child node occupation context N of a current child node according to occupation information of the coded neighboring child node of the current child node; and determining the context I of the current sub-node according to C and N.
Fig. 10 is a schematic diagram of occupancy of a coded neighboring child node of a current child node according to an embodiment of the present invention. The black sub-block is the current sub-node, and the gray sub-block is the coplanar collinear common-point adjacent sub-node of the current sub-node. 1 node contains 8 sub-nodes, and 8a-8h in the figure illustrate co-planar co-linear co-point neighboring sub-nodes at 8 sub-node locations.
Here, the coded neighboring children of the current child node include a total of 7 neighboring children of 3 co-planar, 3 co-linear, and 1 co-point, and the corresponding occupancy information is represented by 7 bits, denoted as C3.
In one possible implementation manner, the determining the neighboring sub-node occupation context of the current sub-node according to the occupation information of the coded neighboring sub-node of the current sub-node includes:
obtaining an information state M according to the occupation information of the coded adjacent child node of the current child node;
and converting the state M through a sliding window to obtain a state N, and taking the state N as the neighboring child node occupation context of the current child node.
Fig. 11 is another schematic diagram of occupancy of a coded neighboring child node of a current child node according to an embodiment of the present invention. The black sub-block is the current sub-node, and the gray sub-block is the adjacent sub-node between the axial directions 1 of the current sub-node. The corresponding occupancy information of 3 inter-axis 1 adjacent sub-nodes is represented by 3 bits and is marked as C4.
Here, the occupancy information M of the coded neighboring child node according to the current child node includes C3 and C4, which have 10 bits in total, and the range of values is 0-1023, which has 1024 states in total. And recording the encoded bit occupation information in each state, adding the latest K encoded bit occupation information through a sliding window to be used as a state N, and using the state N as the neighboring child node occupation context of the current child node.
The value range of the state N is 0-K. If K takes 8, N has 9 state values.
Here, the context i=c× (k+1) +n of the current child node is determined according to the neighboring node occupancy context C and the neighboring child node occupancy context N of the current child node. Where K represents the size of the sliding window.
The invention determines the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node, wherein the combined occupation number considers the occupation number of the neighboring nodes of the multi-class neighboring relationship of the current sub-node, and determines the context according to the geometry structure of the neighboring nodes based on the combined occupation number. Compared with the prior art which only considers the occupation number of single adjacent relations, the invention considers the combined occupation number of adjacent nodes of multiple adjacent relations and the geometric structures of the adjacent nodes under different numbers, and the space distribution correlation of the adjacent nodes is better utilized according to the combined occupation number and the context of geometric structure design, so that the geometric compression performance of the point cloud is improved.
Referring to fig. 12, the present invention provides a point cloud geometric coding method, wherein the point cloud is defined in a tree structure, and each node in the tree structure includes a plurality of child nodes, and the method includes the following steps:
s1: determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node;
s2: and carrying out entropy coding on the occupation information of the current sub-node according to the context of the current sub-node to obtain a point cloud geometric code stream.
Here, step S1 is the same as step D1, and will not be described again.
And performing entropy coding on the occupation information of the current sub-node according to the context of the current sub-node to obtain a point cloud geometric code stream.
In one possible implementation, the determining the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node includes
S11, determining the context of the current sub-node according to the combination occupation number of the coplanar collinear adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes;
s12, or determining the context of the current sub-node according to the geometric structure of the adjacent node and the combined occupation number of the co-planar co-linear co-point adjacent nodes of the current sub-node.
Here, step S11 is the same as step D11, step S12 is the same as step D12, and the description thereof will be omitted.
The invention determines the context of the current sub-node according to the combined occupation number of the neighboring nodes of the current sub-node, wherein the combined occupation number considers the occupation number of the neighboring nodes of the multi-class neighboring relationship of the current sub-node, and determines the context according to the geometry structure of the neighboring nodes based on the combined occupation number. Compared with the prior art which only considers the occupation number of single adjacent relations, the invention considers the combined occupation number of adjacent nodes of multiple adjacent relations and the geometric structures of the adjacent nodes under different numbers, and the space distribution correlation of the adjacent nodes is better utilized according to the combined occupation number and the context of geometric structure design, so that the geometric compression performance of the point cloud is improved.
The following are experimental results.
The experiment is based on PCRM software v9.0 version, and the experiment result of the comparison of the method of the invention with the original sliding window method is tested.
Test conditions: lossy geometry, lossless geometry. The results are shown in tables 2 and 3.
Table 2: comparison of the invention with PCRMv9.0 results
| Lossy geometry | Geometry of |
| Data set 1 | -1.0％ |
| Data set 2 | -1.0％ |
Table 3: comparison of the invention with PCRMv9.0 results
| Lossless geometry | Geometry of |
| Data set 1 | 99.9％ |
| Data set 2 | 96.8％ |
In Table 2, a performance improvement of 1.0% can be seen under geometrically lossy conditions. In Table 3, a 3.2% improvement in performance can be seen under geometrically lossless conditions.
FIG. 13 is a block diagram of an apparatus according to an embodiment of the present invention, as shown in FIG. 13, comprising a processor 20, a memory 22, and a communication bus 24; the memory 22 has stored thereon a computer readable program executable by the processor;
the communication bus 24 enables connection communication between the processor 20 and the memory 22;
the communication bus 24 is connected to a communication interface 23, and the processor 20 implements steps in a point cloud geometry decoding method or an encoding method when executing the computer readable program.
The above examples are only specific embodiments of the present invention for illustrating the technical solution of the present invention, but not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the foregoing examples, it will be understood by those skilled in the art that the present invention is not limited thereto: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for geometric decoding of a point cloud, the point cloud being defined in a tree structure, each node in the tree structure comprising a plurality of child nodes, comprising the steps of:
determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node;
and performing entropy decoding according to the upper and lower Wen Duidian cloud geometric code streams of the current child node to obtain the occupation information of the current child node.
2. The method of point cloud geometry decoding according to claim 1, wherein said determining a context of a current child node from a combined occupancy number of neighboring nodes of the current child node comprises:
determining the context of the current sub-node according to the combination occupation number of the coplanar collinear adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes;
or determining the context of the current sub-node according to the combined occupation number of the co-planar co-linear co-point adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes.
3. The method for decoding point cloud geometry according to claim 2, wherein determining the context of the current sub-node according to the geometry of the neighboring nodes based on the combined occupation number of the neighboring nodes of the current sub-node comprises:
determining the context of the current sub-node according to the combination occupation number of the adjacent nodes of the current sub-node and all the geometric structure types of the adjacent nodes under the combination occupation number;
or determining all geometric structure types of the adjacent nodes according to the combination occupation number of the adjacent nodes of the current sub-node, and reducing the geometric structure types of the adjacent nodes according to the coplanar adjacent node number to determine the context of the current sub-node.
4. The point cloud geometry decoding method of claim 1, wherein determining the context of the current child node from the combined occupancy number of neighboring nodes of the current child node comprises:
determining the context of the neighboring node based on the child node according to the combined occupation number of the neighboring nodes of the current child node;
determining the context of the adjacent node based on the node where the child node is located according to the occupation condition of the adjacent node of the node where the current child node is located;
determining the neighboring node occupation context of the current child node according to the context of the neighboring node based on the child node and the context of the neighboring node based on the node where the child node is located;
and determining the context of the current sub-node according to the occupation context of the neighbor node of the current sub-node.
5. The method of point cloud geometry decoding according to claim 1, wherein said determining a context of a current child node from a combined occupancy number of neighboring nodes of the current child node comprises:
determining the occupation context of the neighbor nodes of the current sub-node according to the combined occupation number of the neighbor nodes of the current sub-node;
determining the occupation context of the neighboring sub-node of the current sub-node according to the occupation information of the coded neighboring sub-node of the current sub-node;
and determining the context of the current sub-node according to the neighboring node occupation context of the current sub-node and the neighboring sub-node occupation context.
6. The method according to claim 5, wherein determining the neighboring sub-node occupation context of the current sub-node according to the occupation information of the encoded neighboring sub-node of the current sub-node comprises:
obtaining an information state M according to the occupation information of the coded adjacent child node of the current child node;
and converting the state M through a sliding window to obtain a state N, and taking the state N as the neighboring child node occupation context of the current child node.
7. A point cloud geometric decoding device, which is characterized by comprising a processor, a memory and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps in the point cloud geometry decoding method according to any of claims 1-6.
8. A method of geometric encoding a point cloud, the point cloud being defined in a tree structure, each node in the tree structure comprising a plurality of child nodes, comprising the steps of:
determining the context of the current sub-node according to the combined occupation number of the adjacent nodes of the current sub-node;
and carrying out entropy coding on the occupation information of the current sub-node according to the context of the current sub-node to obtain a point cloud geometric code stream.
9. The method of point cloud geometric coding according to claim 8, wherein said determining a context of a current child node according to a combined occupancy number of neighboring nodes of the current child node comprises:
determining the context of the current sub-node according to the combination occupation number of the coplanar collinear adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes;
or determining the context of the current sub-node according to the combined occupation number of the co-planar co-linear co-point adjacent nodes of the current sub-node and the geometric structure of the adjacent nodes.
10. The point cloud geometric coding device is characterized by comprising a processor, a memory and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps in the point cloud geometry encoding method of any of claims 8-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211604777.XA CN116094694A (en) | 2022-12-13 | 2022-12-13 | Point cloud geometric coding method, decoding method, coding device and decoding device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211604777.XA CN116094694A (en) | 2022-12-13 | 2022-12-13 | Point cloud geometric coding method, decoding method, coding device and decoding device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116094694A true CN116094694A (en) | 2023-05-09 |
Family
ID=86212899
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211604777.XA Pending CN116094694A (en) | 2022-12-13 | 2022-12-13 | Point cloud geometric coding method, decoding method, coding device and decoding device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116094694A (en) |
-
2022
- 2022-12-13 CN CN202211604777.XA patent/CN116094694A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111699695B (en) | Method, apparatus and storage medium for decoding encoded data sets | |
| US9532056B2 (en) | Method for adaptive entropy coding of tree structures | |
| KR101924249B1 (en) | Hierarchical entropy encoding and decoding | |
| CN113473127B (en) | Point cloud geometric coding method, point cloud geometric decoding method, point cloud geometric coding equipment and point cloud geometric decoding equipment | |
| US8265407B2 (en) | Method for coding and decoding 3D data implemented as a mesh model | |
| US20210021872A1 (en) | Image coding and decoding method and device | |
| CN116506073B (en) | Industrial computer platform data rapid transmission method and system | |
| TWI534760B (en) | Terminable spatial tree-based position coding and decoding | |
| CN111093081A (en) | Lossless image compression method and system | |
| CN115606188A (en) | Point cloud encoding and decoding method, encoder, decoder and storage medium | |
| CN115529357B (en) | Updating abnormity matching method based on MES intercommunication interconnection production data | |
| WO2021196038A1 (en) | Point cloud encoding and decoding methods and devices | |
| CN114222129A (en) | Image compression encoding method, image compression encoding device, computer equipment and storage medium | |
| CN111211787A (en) | Industrial data compression method, system, storage medium and terminal | |
| CN116094694A (en) | Point cloud geometric coding method, decoding method, coding device and decoding device | |
| CN115102934B (en) | Decoding method, encoding device, decoding equipment and storage medium for point cloud data | |
| CN112740707A (en) | Point cloud encoding and decoding method and device | |
| US20230019767A1 (en) | Point cloud encoding method and decoding method, encoder and decoder, and storage medium | |
| CN113099269B (en) | String matching prediction method, encoding and decoding method, related equipment and device | |
| Peng et al. | Progressive geometry encoder using octree-based space partitioning | |
| Jalumuri | A study of scanning paths for BWT-based image compression | |
| WO2023098820A1 (en) | Point cloud coding method and apparatus, point cloud decoding method and apparatus, and communication device | |
| CN114078170A (en) | Method and device for decoding point cloud geometric isolated points | |
| CN115250161A (en) | Data transmission method, device, system and storage medium | |
| CN115474050A (en) | Entropy coding and decoding method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |