Sub-block Entropy Coding for Image Compression
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Solution Overview
Problem
Current video coding methods are inefficient in minimizing the number of bits used for encoding, particularly in entropy coding, as they do not effectively optimize the selection and utilization of entropy coding tables for sub-blocks, leading to suboptimal compression and increased signaling bits.
Innovation Solution
A sub-block entropy coding method that partitions image blocks into sub-blocks, selects the most optimal combination of entropy coding tables based on statistical analysis, and reduces the number of tables by eliminating less frequently used combinations, thereby minimizing the number of bits required for encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple entropy coding tables are used for each sub-block, then encoding flexibility and optimization are improved, but the number of signaling bits increases
Solution Approach 1:
The image block is divided into multiple sub-blocks, and entropy coding tables are selected independently for each sub-block based on its statistical characteristics. This segmentation allows flexible adaptation to local content variations while managing signaling overhead through hierarchical selection mechanisms.
Solution Approach 2:
The patent changes the parameter of entropy coding table selection by using statistical analysis results to dynamically choose appropriate tables for each sub-block. This parameter change enables adaptation to different content types (e.g., smooth regions vs. detailed regions) while controlling signaling bits through intelligent selection criteria.
2Quantity of substance
If entropy coding tables are reduced based on statistical analysis, then the number of signaling bits is reduced, but encoding flexibility may be compromised
Solution Approach 1:
Statistical analysis is performed on the image content to determine appropriate entropy coding tables for each sub-block. This parameter change approach reduces the number of tables that need to be signaled while maintaining encoding flexibility by selecting tables based on actual content characteristics rather than providing all possible options.
Solution Approach 2:
The system performs self-service by automatically analyzing statistical properties of each sub-block and selecting appropriate entropy coding tables without requiring extensive signaling to the decoder. The encoder and decoder both perform the same statistical analysis, eliminating the need to signal table selection decisions.
3Device complexity
If traditional entropy coding methods are used without sub-block partitioning, then device complexity is reduced, but compression efficiency deteriorates
Solution Approach 1:
The patent segments the image block into multiple sub-blocks and applies entropy coding independently to each sub-block using statistically optimized table selection. This segmentation improves compression efficiency by adapting to local content variations while maintaining manageable device complexity through systematic processing of smaller units.
Solution Approach 2:
The patent changes the coding approach by performing statistical analysis at the sub-block level and selecting entropy coding tables accordingly. This parameter change improves compression efficiency by matching coding parameters to local content characteristics while keeping device complexity acceptable through automated selection processes.
Data Source
AI summary
A sub-block entropy coding method more efficiently encodes content. Specifically, by selecting the most optimal tables for each sub-block, the number of bits utilizes is minimized. Furthermore, based on results, tables are able to be eliminated as options to further reduce the number of signaling bits.