Point Cloud Occupancy Coding With Neighbor-Based Binary Contexts
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Solution Overview
Problem
Current methods for compressing point clouds are inefficient and do not effectively manage contexts, leading to suboptimal compression performance.
Innovation Solution
The proposed solution involves a method for encoding and decoding point clouds using binary entropy coding, where the probability for coding bits is determined based on a sub-volume neighbour configuration that depends on the occupancy pattern of neighbouring volumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression methods are used for point clouds, then implementation is simpler, but compression efficiency is suboptimal
Solution Approach 1:
The patent segments the point cloud data into multiple volumes and further divides each volume into sub-volumes. This segmentation allows for independent context management in each sub-volume, reducing the overall complexity while improving compression efficiency through localized adaptive coding.
Solution Approach 2:
The patent introduces a spatial dimension to context management by using sub-volume neighbour configurations. Instead of managing contexts in a single sequence, the system considers the three-dimensional spatial relationships between neighbouring sub-volumes, enabling more efficient probability estimation for binary entropy coding.
2Measurement precision
If more contexts are managed for binary entropy coding, then coding accuracy improves, but computational overhead increases
Solution Approach 1:
The patent applies local quality by determining sub-volume neighbour configurations specific to each sub-volume's spatial position. Each sub-volume uses its own localized neighbour configuration for probability estimation, which improves coding accuracy for that specific region while avoiding the need to manage all possible contexts globally, thus reducing computational overhead.
3Productivity
If conventional encoding methods are used, then implementation is faster, but compression performance is suboptimal
Solution Approach 1:
The patent performs preliminary actions by organizing the point cloud data into a hierarchical volume and sub-volume structure before encoding. The sub-volume neighbour configurations are determined in advance based on spatial relationships, which facilitates more efficient binary entropy coding without requiring complex real-time computations during the encoding process.
Data Source
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.