Transform Coefficient Coding With Unified Adaptive Context Modeling
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Solution Overview
Problem
Existing image and video codecs face challenges in maintaining high coding efficiency while managing complexity due to varying transform block sizes and additional data components like depth maps and chroma components, necessitating numerous context-dependent symbolization schemes.
Innovation Solution
A context-adaptive entropy coding method that uses a common function to determine the context and symbolization parameter based on previously coded/decoded transform coefficients, allowing for efficient coding of transform coefficients across different block sizes and frequency portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple different symbolization schemes are used to adapt to various coefficient statistics, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by using a single symbolization scheme with adjustable parameters (such as interval boundaries and probability models) that are adapted to different coefficient statistics. Instead of maintaining multiple fixed schemes, the system modifies parameters like the number of intervals, interval boundaries, and probability distribution parameters based on the statistical characteristics of the current coefficient block, thereby achieving adaptability with reduced complexity.
Solution Approach 2:
The patent implements dynamics by making the symbolization scheme adaptive and configurable rather than static. The scheme can dynamically adjust its parameters based on the statistical properties of transform coefficients, allowing the same basic structure to serve multiple statistical scenarios. This dynamic adaptation replaces the need for multiple predetermined schemes.
2Productivity
If numerous contexts with different functions are used to determine context from already coded transform coefficients, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a unified context determination function that serves multiple purposes across different coding scenarios. Instead of implementing separate context models for different block sizes, frequency portions, and coefficient types, the system uses a single versatile function with configurable parameters that can adapt to various contexts. This multi-functional approach reduces the number of separate context structures needed.
Solution Approach 2:
The context determination function uses parameter changes to adapt to different coding scenarios. By modifying parameters such as the weighting factors, neighbor coefficient selections, and function coefficients based on the current block characteristics, the same function can effectively model different contexts without requiring separate dedicated functions for each scenario.
3Device complexity
If a common function is used to determine context and symbolization parameter, then device complexity is reduced, but adaptability to different coefficient statistics decreases
Solution Approach 1:
The common function achieves adaptability through dynamics by allowing its parameters to be adjusted based on the statistical characteristics of the coefficient block. The function structure remains unified and simple, but its behavior adapts to different scenarios through parameter modification, such as changing the influence weights of neighboring coefficients or adjusting the function coefficients based on observed statistics.
Solution Approach 2:
The patent resolves this contradiction by enabling parameter changes within the common function. Parameters such as the function coefficients, weighting factors, and threshold values can be modified based on the statistical properties of the current coefficient block, allowing the same functional structure to adapt to different coefficient distributions while maintaining structural simplicity.
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.