Transform Coefficient Coding With Unified Context Adaptation
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Solution Overview
Problem
Existing image and video codecs face challenges in achieving high compression efficiency while managing complexity due to varying transform block sizes and additional data components like depth maps and chroma values, necessitating numerous symbolization schemes that increase computational demands.
Innovation Solution
A context-adaptive entropy coding approach that uses a common function for context selection and symbolization parameter determination, adapting to previously coded/decoded transform coefficients, regardless of block size or type, to optimize coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple different symbolization schemes are used to adapt to varying transform block sizes and data components, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by using a single unified symbolization scheme that can handle multiple data components (luma, chroma, depth, transparency) and various transform block sizes through parameter adjustment rather than requiring separate schemes for each case. This single scheme performs multiple functions that previously required multiple specialized schemes.
Solution Approach 2:
The patent uses parameter changes by adjusting symbolization parameters (such as interval boundaries and mapping rules) based on the specific characteristics of the current transform coefficients, including block size and data component type. This allows one flexible scheme to adapt to various scenarios without requiring multiple rigid schemes.
2Productivity
If context-adaptive entropy coding is used to precisely estimate symbol probabilities, then coding efficiency is improved, but computational overhead increases
Solution Approach 1:
The patent adjusts context parameters and probability estimation parameters based on the actual statistics of previously coded transform coefficients. By dynamically changing these parameters to match the current data characteristics, the system achieves better coding efficiency without requiring excessively complex computational models.
Solution Approach 2:
The patent implements feedback by using previously coded transform coefficients to update context probabilities and symbolization parameters for current coefficients. This feedback mechanism allows the system to adapt to local statistics and improve coding efficiency while keeping the computational model manageable through iterative refinement.
Data Source
AI summary
An idea used herein is to use the same function for the dependency of the context and the dependency of the symbolization parameter on previously coded/decoded transform coefficients. Using the same function—with varying function parameter—may even be used with respect to different transform block sizes and/or frequency portions of the transform blocks in case of the transform coefficients being spatially arranged in transform blocks. A further variant of this idea is to use the same function for the dependency of a symbolization parameter on previously coded/decoded transform coefficients for different sizes of the current transform coefficient's transform block, different information component types of the current transform coefficient's transform block and/or different frequency portions the current transform coefficient is located within the transform block.