Transform Coefficient Coding Using Unified Symbolization Parameters
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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, due to increased demands and diverse contexts in transform coefficient coding.
Innovation Solution
The implementation of a context-adaptive entropy decoder and encoder that uses a parametrizable function for context selection and symbolization parameter determination, allowing for efficient decoding and encoding of transform coefficients across different block sizes and information component types, by mapping symbols onto multiple level intervals with varying symbolization schemes.
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 due to the huge amount of differing symbolization schemes required
Solution Approach 1:
The patent applies parameter changes by introducing a symbolization parameter that controls the transition between different symbolization schemes. Instead of implementing multiple complete symbolization schemes, the system changes parameters (threshold values, binarization methods) based on transform coefficient level magnitudes and positions, achieving adaptation to different coefficient statistics while maintaining a single unified coding framework. This resolves the contradiction by achieving coding efficiency through parameter variation rather than through complex multi-scheme implementation.
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 for variable block sizes and additional data types
Solution Approach 1:
The patent implements universality by designing a unified context adaptation mechanism that serves multiple functions: it handles different transform block sizes (4×4, 8×8, 16×16), different coefficient types (luma, chroma, depth), and different position-based statistics all through a single context model. The context is adapted based on universal factors like coefficient level magnitude and position relative to significant coefficients, rather than requiring separate contexts for each block size or data type. This single context model performs multiple functions, resolving the contradiction between coding efficiency and context management complexity.
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.