Point Cloud Octree Context Encoding Using Neighbor Occupancy
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Solution Overview
Problem
Current methods for encoding and decoding point clouds do not effectively utilize information from neighboring nodes, leading to inefficient data compression.
Innovation Solution
An encoding and decoding method that considers the occupancy of neighboring and sibling nodes in an octree-based structure to determine context information for entropy encoding, enhancing data reduction by improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding is used without considering neighboring node information, then the encoding process is simple, but the compression efficiency is poor
Solution Approach 1:
The patent performs preliminary analysis of neighboring and sibling node occupancies before entropy encoding the current node. By pre-determining context information from adjacent nodes in the octree structure, the encoding process benefits from prepared probability estimates, improving compression efficiency without adding significant complexity during the actual encoding phase
Solution Approach 2:
The patent implements a feedback mechanism where occupancy information from neighboring and sibling nodes is used to dynamically determine context information for the current node. This feedback loop allows the encoder to adapt probability models based on local spatial patterns, significantly improving compression efficiency by exploiting spatial correlations in point cloud data
2Measurement precision
If context information from neighboring and sibling nodes is considered, then prediction accuracy improves, but the amount of context information to process increases
Solution Approach 1:
The patent applies local quality by considering only the occupancy information of directly adjacent neighboring nodes and sibling nodes in the octree structure, rather than processing information from all nodes. This localized approach focuses computational resources on the most relevant spatial neighbors, improving prediction accuracy while keeping the quantity of context information manageable through selective processing
Data Source
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AI summary
A method for encoding and decoding, an encoder (10) and decoder (20) for a point cloud is disclosed. The method for encoding a point cloud to generate a bitstream (14,26) of compressed point cloud data (12,28), wherein the point cloud's geometry is represented by an octree-based structure with a plurality of nodes having parent-child relationships by recursively splitting a volumetric space containing the point cloud into sub-volumes each associated with a node of the octree-based structure, comprising the steps of: determining a context information of a current node (120) including plane position context information, wherein the plane position context information of the current node (120) is determined according to the occupancy of a neighboring node (126) directly adjacent the current node (120) and the occupancy of at least one sibling node (134) having the same parent node (132) as the current node (120) (S110); and at least entropy encoding the occupancy parent node based on the determined context information to produce encode (S120).