Point Cloud Binary Entropy Coding With Occupancy Context Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for compressing point clouds are inefficient and do not effectively manage contexts, leading to suboptimal compression and decoding performance, particularly in handling the geometry of sparse point clouds.
Innovation Solution
The use of context-adaptive binary entropy coding for point clouds, where probabilities for entropy encoding and decoding are selected based on occupancy data from neighboring nodes and child nodes, reducing the number of contexts through context reduction operations 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 segments the occupancy pattern coding into multiple binary decisions (one bit per child node occupancy), where each binary decision uses a separate context. This segmentation allows the use of binary entropy coding while managing context complexity by breaking down the non-binary coding problem into manageable binary steps.
Solution Approach 2:
The patent performs preliminary determination of the occupancy pattern for the current node based on child node occupancy statuses before selecting contexts for entropy coding. This preliminary action allows the system to prepare and select appropriate contexts in advance, reducing the computational burden during the actual coding process.
2Productivity
If probabilities are selected based on occupancy data from neighboring nodes and child nodes, then compression performance is improved, but coding complexity increases
Solution Approach 1:
The patent applies local quality by selecting different probability values based on the specific occupancy configuration of neighboring nodes and child nodes. Each local context (defined by the occupancy pattern of neighbors and children) gets a tailored probability selection, optimizing compression for that specific local configuration rather than using a single global probability model.
Solution Approach 2:
The patent uses feedback from occupancy data of previously coded neighboring nodes and child nodes to inform the probability selection for the current node's occupancy pattern. This feedback mechanism allows the coding process to adapt to the local geometry and occupancy characteristics, improving compression efficiency while maintaining manageable complexity through systematic feedback utilization.
Data Source
AI summary
Methods and devices for encoding or decoding a point cloud. A bit sequence signaling 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.