Alphabet-Partition Coding of Point Cloud Coefficients
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Solution Overview
Problem
Current graph-based point cloud compression (G-PCC) methods face inefficiencies in compression efficiency when there are not enough neighboring attribute samples available for prediction, particularly in scenarios requiring lossy and lossless compression for real-time communications and dynamic mapping applications.
Innovation Solution
The method involves decomposing transform coefficients into set-index values and symbol-index values, partitioning them into sets, and using entropy-coding for set-index values while bypass-coding symbol-index values, improving compression efficiency through alphabet-partitioning and coding of alphabet-partition information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current G-PCC methods use prediction based on neighboring attribute samples, then compression efficiency can be improved when sufficient neighbors are available, but compression efficiency deteriorates when not enough neighboring samples are available
Solution Approach 1:
The transform coefficients are segmented into multiple sets based on their magnitude ranges (e.g., first set for coefficients with absolute values greater than a threshold, second set for coefficients with absolute values less than or equal to the threshold). This segmentation allows different coding strategies to be applied to different sets, improving overall compression efficiency by adapting to the statistical characteristics of each set independently.
Solution Approach 2:
Different coding methods are applied to different sets of transform coefficients based on their local characteristics. The first set (larger coefficients) and second set (smaller coefficients) are coded differently, with each set optimized for its specific statistical properties. This local quality approach ensures that each coefficient group is encoded with the most appropriate method for its magnitude range.
2Productivity
If transform coefficients are coded using traditional entropy coding methods, then coding simplicity is maintained, but compression efficiency is suboptimal for point cloud data with specific statistical characteristics
Solution Approach 1:
The transform coefficient sequence is divided into multiple sets based on magnitude thresholds. Each set is then coded independently using entropy coding, allowing the coder to exploit the different statistical properties of different coefficient ranges. This segmentation improves compression efficiency by adapting the coding strategy to the local statistics of each set.
Solution Approach 2:
The invention changes the parameter being coded from the raw transform coefficient values to the set indices that indicate which magnitude range each coefficient falls into. This parameter transformation allows the use of entropy coding on a discrete, bounded variable (the set index) rather than on the continuous transform coefficient values, improving compression efficiency while maintaining reasonable coding complexity.
Data Source
AI summary
A method, apparatus, and computer-readable medium for point cloud coefficient coding are provided. Transform coefficients associated with point cloud data are decomposed into set-index values and symbol-index values, the symbol index-value specifying location of the transform coefficient within a set. The decomposed transform coefficients are partitioned into one or more sets based on the set-index values and the symbol-index values. The set-index values of the partitioned transform coefficients are entropy-coded, and the symbol-index values of the partitioned transform coefficients are bypass-coded. The point cloud data is compressed based on the entropy-coded symbol-index values and the bypass-coded set-index values.