Point Cloud Entropy Coding With Reduced Context Complexity
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Solution Overview
Problem
Current point cloud compression methods are inefficient and do not effectively manage contexts, particularly in context-adaptive binary entropy coding, leading to suboptimal compression of large datasets in three-dimensional object representations.
Innovation Solution
The proposed solution involves encoding and decoding point clouds using a method that selects probabilities for entropy coding based on occupancy data from neighboring nodes and child nodes, employing context reduction operations to reduce the number of contexts required, and utilizing binary entropy coding to improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-adaptive binary entropy coding is used for point cloud compression, then compression efficiency is improved, but the number of contexts to be managed increases excessively
Solution Approach 1:
The patent merges multiple context models into a unified context adaptation mechanism. Instead of maintaining separate context models for different node types and positions, the invention combines them into a single adaptive context system that dynamically adjusts probabilities based on local occupancy patterns, thereby reducing the total number of contexts while preserving compression efficiency
Solution Approach 2:
The patent implements dynamic context adaptation where probability models are continuously adjusted based on local occupancy patterns rather than using static context assignments. The context probabilities are dynamically updated according to the occupancy status of neighboring nodes, allowing the system to adapt to local variations in point cloud density without requiring an excessive number of pre-defined contexts
2Productivity
If more contexts are used in entropy coding, then compression performance is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by using different probability models only where necessary - specifically adapting contexts based on local occupancy patterns in regions where variations occur, while using simpler models in uniform regions. This localized adaptation improves compression performance in complex areas without unnecessarily increasing computational complexity throughout the entire point cloud
Solution Approach 2:
The patent changes the parameters of the entropy coding model dynamically based on local occupancy patterns. Instead of using a fixed large number of contexts, the system adjusts probability parameters adaptively according to the local density and distribution of points, achieving good compression performance with reduced computational overhead
Data Source
AI summary
Methods and devices for encoding or decoding a point cloud. A bit sequence signalling an occupancy pattern for sub-volumes of a volume is coded using entropy coding. For a current sub-volume, probabilities of respective entropy coders for entropy coding the occupancy pattern may be selected based on occupancy data for a plurality of neighbouring sub-volumes of the current sub-volume and on occupancy data for subdivisions of the neighbouring sub-volumes.