Transform Coefficient Coding With Unified Context Adaptation
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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 variable transform block sizes and the need to convey additional data such as depth maps and transparity values, which increases the number of contexts and requires precise adaptation of symbolization schemes to coefficient statistics.
Innovation Solution
An apparatus and method that use a context-adaptive entropy decoder and encoder with parameterizable symbolization schemes to map transform coefficients onto symbols, employing a common function for context selection and symbolization parameter determination, adaptable to different block sizes and information components, to efficiently encode and decode transform coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple different symbolization schemes are used to precisely adapt to coefficient statistics, then coding efficiency is improved, but device complexity increases due to the huge amount of differing symbolization schemes required
Solution Approach 1:
The patent applies parameter changes by using a single symbolization scheme with adjustable parameters (such as interval boundaries and mapping characteristics) that can be dynamically adapted to match different coefficient statistics. Instead of maintaining multiple fixed symbolization schemes, the system modifies parameters of one scheme to achieve the same adaptive effect, thereby improving coding efficiency while controlling device complexity.
2Productivity
If contexts are used to precisely estimate the probability of transform coefficient levels, then coding efficiency is improved, but device complexity increases due to the increasing number of different contexts required
Solution Approach 1:
The patent applies universality by designing a unified context structure that serves multiple functions. Instead of creating separate contexts for different transform block sizes and coefficient types, the system uses a single context model that can be parameterized to handle various scenarios. This allows the same context to be reused across different situations, reducing the total number of contexts needed while maintaining precise probability estimation for coding efficiency.
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.