Transform Coefficient Coding Using Unified Context and Symbolization
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Solution Overview
Problem
Existing image and video codecs face challenges in maintaining low complexity while achieving high coding efficiency, especially with varying block sizes and additional data components like depth maps, which require multiple contexts and symbolization schemes to adapt to changing statistics.
Innovation Solution
An apparatus and method that use a context-adaptive entropy decoder and symbolizer to decode and encode transform coefficients, employing a single function for context selection and symbolization parameter determination, which varies based on previously coded/decoded coefficients, to efficiently handle different block sizes and information types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple contexts and symbolization schemes are used to adapt to varying 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 function that serves multiple purposes: it selects contexts for entropy coding and determines symbolization parameters simultaneously. This unified function handles varying block sizes and different data components (luma, chroma, depth maps) through a single mechanism rather than requiring separate contexts and schemes for each case, thereby reducing device complexity while maintaining adaptability.
Solution Approach 2:
The patent uses parameter changes by making the unified function parameterizable, where the function parameter can be set to different values to adapt to different coding scenarios (different block sizes, different data components). This allows the same function structure to handle varying conditions by changing its parameters rather than requiring fundamentally different contexts or schemes.
2Device complexity
If a single function is used for context selection and symbolization parameter determination, then device complexity is reduced, but adaptability to varying statistics may be compromised
Solution Approach 1:
The patent applies dynamics by making the unified function adaptive to previously coded/decoded transform coefficients. The function dynamically adjusts its behavior based on the actual statistics observed in the data, allowing it to adapt to varying block sizes and data components in real-time processing without requiring a static, pre-configured set of contexts.
Solution Approach 2:
The patent uses feedback by incorporating previously coded/decoded transform coefficients into the unified function's decision-making process. The function uses this feedback information to adaptively select contexts and determine symbolization parameters, ensuring that the single function can effectively adapt to varying statistics through continuous learning from the coded data itself.
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.