US-20260129188-A1 - METHOD FOR ENCODING, METHOD FOR DECODING, AND STORAGE MEDIUM

US20260129188A1US 20260129188 A1US20260129188 A1US 20260129188A1US-20260129188-A1

Abstract

A method for decoding includes: a prediction mode corresponding to a current node is determined; in a case where the prediction mode is a first mode, neighbouring nodes corresponding to the current node are determined in a current picture, and a reference node corresponding to the current node is determined in a reference picture corresponding to the current picture; and a prediction value of the current node is determined according to a first prediction value of the reference node and a second prediction value of the neighbouring nodes.

Inventors

Zexing SUN

Assignees

GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., LTD.

Dates

Publication Date: 20260507
Application Date: 20260102

Claims (20)

1 . A method for decoding, applied to a decoder, the method comprising: determining a prediction mode corresponding to a current node; in a case where the prediction mode is a first mode, determining neighbouring nodes corresponding to the current node in a current picture, and determining a reference node corresponding to the current node in a reference picture corresponding to the current picture; and determining a prediction value of the current node according to a first prediction value of the reference node and a second prediction value of the neighbouring nodes.
2 . The method according to claim 1 , wherein determining the prediction mode corresponding to the current node comprises: decoding a bitstream to determine prediction mode identification information corresponding to a current layer; and determining the prediction mode corresponding to the current node in the current layer according to the prediction mode identification information.
3 . The method according to claim 2 , wherein determining the prediction mode corresponding to the current node in the current layer according to the prediction mode identification information comprises: in a case where a value of the prediction mode identification information is a first value, determining that the prediction mode corresponding to the current node is the first mode; and in a case where the value of the prediction mode identification information is a second value, determining that the prediction mode corresponding to the current node is a second mode.
4 . The method according to claim 1 , wherein determining the prediction mode corresponding to the current node comprises: determining an error parameter between the current node and the reference node according to occupancy information of the current node and occupancy information of the reference node; and determining the prediction mode according to the error parameter and an error threshold.
5 . The method according to claim 4 , wherein determining the prediction mode according to the error parameter and the error threshold comprises: in a case where the error parameter is greater than or equal to the error threshold, determining that the prediction mode corresponding to the current node is the first mode; and in a case where the error parameter is less than the error threshold, determining that the prediction mode corresponding to the current node is a second mode.
6 . The method according to claim 2 , wherein the first mode is a prediction mode in which intra prediction and inter prediction are weighted; and the second mode is a prediction mode in which intra prediction and inter prediction are combined.
7 . The method according to claim 1 , wherein determining the prediction value of the current node according to the first prediction value of the reference node and the second prediction value of the neighbouring nodes comprises: determining a first weight corresponding to the first prediction value, and determining a second weight corresponding to the second prediction value; and performing a weighted calculation on the first prediction value and the second prediction value according to the first weight and the second weight, to determine the prediction value of the current node.
8 . The method according to claim 7 , wherein determining the first weight corresponding to the first prediction value, and determining the second weight corresponding to the second prediction value comprises: decoding a bitstream to determine the first weight and the second weight.
9 . The method according to claim 7 , wherein determining the first weight corresponding to the first prediction value, and determining the second weight corresponding to the second prediction value comprises: determining temporal interval information between the current picture and the reference picture; determining a first initial weight corresponding to the first prediction value, and determining a second initial weight corresponding to the second prediction value; and determining the first weight according to the temporal interval information and the first initial weight, and determining the second weight according to the temporal interval information and the second initial weight.
10 . The method according to claim 7 , wherein determining the first weight corresponding to the first prediction value, and determining the second weight corresponding to the second prediction value comprises: determining the first weight according to a time slot distance between the current picture and the reference picture; and determining the second weight according to a number of neighbouring nodes corresponding to the current node.
11 . The method according to claim 4 , further comprising: in a case where the prediction mode is the second mode, if the reference node corresponding to the current node exists in the reference picture corresponding to the current picture, determining the prediction value of the current node according to the first prediction value of the reference node.
12 . The method according to claim 4 , further comprising: in a case where the prediction mode is the second mode, if the reference node corresponding to the current node does not exist in the reference picture corresponding to the current picture, determining the prediction value of the current node according to the second prediction value of the neighbouring nodes.
13 . The method according to claim 1 , further comprising: performing a determination process for the prediction mode of the current node in a case where attribute prediction for the current node is allowed and inter attribute prediction for the current node is allowed.
14 . The method according to claim 13 , further comprising: determining a neighbourhood node number of the current node and a parent node neighbourhood node number of the current node; and determining that attribute prediction for the current node is allowed in a case where the neighbourhood node number of the current node is greater than a first threshold and the parent node neighbourhood node number is greater than a second threshold.
15 . The method according to claim 2 , wherein the prediction mode identification information corresponding to the current layer is a syntax element corresponding to an Attribute Block Head (ABH).
16 . A method for encoding, applied to an encoder, the method comprising: determining a prediction mode corresponding to a current node according to a rate-distortion optimization algorithm; or determining the prediction mode corresponding to the current node according to a correlation between the current node and a reference node corresponding to the current node in a reference picture, wherein the prediction mode comprises a first mode and a second mode.
17 . The method according to claim 16 , wherein determining the prediction mode corresponding to the current node according to the rate-distortion optimization algorithm comprises: determining a first cost value corresponding to the first mode and a second cost value corresponding to the second mode according to the rate-distortion optimization algorithm; and determining the prediction mode corresponding to the current node according to the first cost value and the second cost value.
18 . The method according to claim 17 , further comprising: in a case of determining that the prediction mode corresponding to the current node is the first mode, determining that a value of prediction mode identification information corresponding to a current layer is a first value; in a case of determining that the prediction mode corresponding to the current node is the second mode, determining that the value of the prediction mode identification information corresponding to the current layer is a second value; and signalling the prediction mode identification information in a bitstream.
19 . The method according to claim 16 , wherein determining the prediction mode corresponding to the current node according to a correlation between the current node and the reference node corresponding to the current node in the reference picture comprises: determining an error parameter between the current node and the reference node according to occupancy information of the current node and occupancy information of the reference node; and determining the prediction mode according to the error parameter and an error threshold.
20 . A non-transitory computer-readable storage medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform operations to generate the bitstream, wherein the operations comprise: determining a prediction mode corresponding to a current node according to a rate-distortion optimization algorithm; or determining the prediction mode corresponding to the current node according to a correlation between the current node and a reference node corresponding to the current node in a reference picture, wherein the prediction mode comprises a first mode and a second mode.

Description

CROSS REFERENCE TO RELATED APPLICATION This is a continuation of International Application No. PCT/CN2023/106646 filed on Jul. 10, 2023, the disclosure of which is hereby incorporated by reference in its entirety. TECHNICAL FIELD Embodiments of the present disclosure relate to the technical field of point cloud encoding and decoding, in particular to a method for encoding, a method for decoding, and a storage medium. BACKGROUND In a Geometry-based Point Cloud Compression (G-PCC) encoding and decoding framework, geometry information and attribute information of a point cloud are encoded separately. The attribute coding of G-PCC may include: Prediction Transform (PT), Lifting Transform (LT), and Region Adaptive Hierarchical Transform (RAHT). However, the common attribute prediction coding scheme does not take into account the correlation between inter and intra prediction and the attribute value of the current node, which leads to low RAHT attribute coding efficiency of the current node cloud, reduces the prediction effect of the attribute information, and reduces the encoding and decoding performance of the point cloud. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1A is a schematic diagram of a three-dimensional (3D) point cloud picture; FIG. 1B is a partial enlargement diagram of a three-dimensional point cloud picture; FIG. 2A is a schematic diagram of six viewing angles of a point cloud picture; FIG. 2B is a schematic diagram of a data storage format corresponding to a point cloud picture; FIG. 3 is a schematic diagram of network architecture of point cloud encoding and decoding; FIG. 4A is a schematic diagram of a composition framework of a G-PCC encoder; FIG. 4B is a schematic diagram of a composition framework of a G-PCC decoder; FIG. 5A is a schematic diagram of a bottom virtual plane position in a Z-axis direction; FIG. 5B is a schematic diagram of a top virtual plane position in a Z-axis direction; FIG. 6 is a schematic diagram of a node encoding order; FIG. 7A is a schematic diagram of plane identification information; FIG. 7B is another schematic diagram of plane identification information; FIG. 8 is a schematic diagram of a sibling node of a current node; FIG. 9 is a schematic diagram of an intersection of a Lidar and a node; FIG. 10 is a schematic diagram of a neighbourhood node at a same partitioning depth and at a same coordinates; FIG. 11A is a schematic diagram of a current node located at a bottom virtual plane position of a parent node; FIG. 11B is another schematic diagram of a current node located at a bottom virtual plane position of a parent node; FIG. 11C is yet another schematic diagram of a current node located at a bottom virtual plane position of a parent node; FIG. 12A is a schematic diagram of a current node located at a top virtual plane position of a parent node; FIG. 12B is another schematic diagram of a current node located at a top virtual plane position of a parent node; FIG. 12C is yet another schematic diagram of a current node located at a top virtual plane position of a parent node; FIG. 13 is a schematic diagram of prediction coding of Lidar point cloud plane position information; FIG. 14 is a schematic diagram of Infer Direct Coding Model (IDCM) coding; FIG. 15 is a schematic diagram of coordinate conversion of a point cloud obtained by a rotating Lidar; FIG. 16 is a schematic diagram of prediction coding in an X-axis or Y-axis direction; FIG. 17A is a schematic diagram of predicting an angle of a Y-plane by a horizontal azimuth angle; FIG. 17B is a schematic diagram of predicting an angle of an X-plane by a horizontal azimuth angle; FIG. 18 is another schematic diagram of prediction coding in an X-axis or Y-axis direction; FIG. 19A is a schematic diagram of three vertices included in a sub-block; FIG. 19B is a schematic diagram of a triangle soup (trisoup) fitted using three vertices; FIG. 19C is a schematic diagram of upsampling a trisoup; FIG. 20 is a schematic diagram of a distance-based Level of Detail (LOD) construction process; FIG. 21 is a schematic diagram of a visualization result of a LOD generation process; FIG. 22 is a schematic diagram of an encoding process of an attribute prediction; FIG. 23 is a schematic diagram of a composition of a pyramid structure; FIG. 24 is a schematic diagram of a composition of another pyramid structure; FIG. 25 is a schematic diagram of an LOD structure of an inter-layer nearest neighbour search; FIG. 26 is a schematic diagram of a nearest neighbour search structure based on a spatial relationship; FIG. 27A is a schematic diagram of a coplanar spatial relationship; FIG. 27B is a schematic diagram of a coplanar and collinear spatial relationship; FIG. 27C is a schematic diagram of a coplanar, collinear, and co-point spatial relationship; FIG. 28 is a schematic diagram of an inter-layer prediction based on fast search; FIG. 29 is a schematic structural diagram of LOD of an attribute intra-layer nearest neighbour search; FIG. 30 is