Point Cloud Occupancy Coding With Neighbor-Based Context Reduction
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Solution Overview
Problem
Current point cloud compression methods are inefficient in handling large datasets and fail to effectively manage contexts in context-adaptive binary entropy coding, leading to suboptimal compression and decoding performance.
Innovation Solution
The proposed solution involves encoding and decoding point clouds using a method that determines bit sequences for sub-volumes based on neighbor configurations, applying context reduction operations to reduce the number of contexts, and using binary entropy coding to improve compression efficiency while maintaining computational feasibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If context-adaptive binary entropy coding is applied to 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 point cloud data into multiple octants (8 sub-volumes) and processes each octant separately with its own context model. This segmentation allows the system to manage a limited number of contexts per octant rather than requiring excessive contexts for the entire point cloud, thus resolving the contradiction between compression efficiency and context management complexity
Solution Approach 2:
The patent applies local quality by using neighbor configuration-dependent contexts where each context model is adapted to the specific local geometry of neighboring points. This allows efficient compression by exploiting local spatial correlations without requiring a globally excessive number of contexts, as each local region uses contexts tailored to its specific characteristics
2Loss of information
If more contexts are used in binary entropy coding, then compression performance is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-defining a limited set of context models based on neighbor configurations before the actual entropy coding process. This allows the system to achieve good compression performance using a manageable number of pre-prepared contexts, avoiding the need to compute and manage excessive contexts during compression, thus reducing computational complexity while maintaining performance
3Quantity of substance
If point cloud data is compressed using existing methods, then data storage is reduced, but compression and decoding speed is insufficient for large datasets
Solution Approach 1:
The patent segments the point cloud into multiple octants and processes each independently with parallelizable operations. This segmentation enables efficient compression and decoding of large datasets by allowing parallel processing of different octants, thus improving productivity while achieving effective data storage reduction
Solution Approach 2:
The patent applies partial action by focusing computational resources on encoding the most significant geometric features using neighbor configuration-dependent contexts. This approach achieves effective compression for large datasets by prioritizing the encoding of critical spatial relationships, improving processing speed without sacrificing essential point cloud information
Data Source
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AI summary
Methods and devices for encoding a point cloud. A bit sequence signalling an occupancy pattern for sub-volumes of a volume is coded using binary entropy coding. For a given bit in the bit sequence, a context may be based on a sub-volume neighbour configuration for the sub-volume corresponding to that bit. The sub-volume neighbour configuration depends on an occupancy pattern of a group of sub-volumes of neighbouring volumes to the volume, the group of sub-volumes neighbouring the sub-volume corresponding to the given bit. The context may be further based on a partial sequence of previously-coded bits of the bit sequence.