Unified Significance Map Coding With Shared Context Models
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Solution Overview
Problem
Existing video coding standards, such as MPEG-4 AVC and KTA, face challenges in effectively capturing the difference in coefficient distributions across various transform sizes during significance map coding, leading to inefficient context modeling and difficulty in extending designs to future standards with more transforms.
Innovation Solution
A unified rule-based approach is introduced for generating context sharing maps that adapt to different transform sizes, allowing multiple transform coefficient positions to share contexts, simplifying the design of encoders and decoders, and enabling easy extension to future standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate context models are used for each transform size, then the coefficient distribution can be accurately captured, but the device complexity and number of context models increase significantly
Solution Approach 1:
The patent merges context models across different transform sizes by introducing a context sharing mechanism. Contexts are shared between 4×4, 8×8, 16×16, and other transform sizes using a unified context sharing map, reducing the total number of context models while maintaining adaptive coding performance across various transform types.
Solution Approach 2:
The patent creates a universal context modeling framework where a single set of context models serves multiple transform sizes. The context sharing map enables one context model to be universally applied across different transform sizes (4×4, 8×8, 16×16, etc.), making the system multi-functional and reducing complexity.
2Manufacturing precision
If separate context modeling designs are created for each transform size, then specific optimization can be achieved, but the ease of extension to future standards with more transforms is reduced
Solution Approach 1:
The patent establishes a universal context sharing mechanism that works across all transform sizes including future transforms. The unified context sharing map and standardized context indexing allow the system to easily accommodate new transform types without requiring separate design efforts, enhancing future-proofing and adaptability.
Solution Approach 2:
The patent introduces a dynamic context sharing approach where the context selection adapts based on the actual transform size being used. The context sharing map dynamically maps contexts for different transform sizes, allowing the system to flexibly handle current and future transform types with a single unified framework.
3Device complexity
If limited context templates are used, then the model cost is reduced, but the measurement precision of conditional probability estimation deteriorates
Solution Approach 1:
The patent combines context information from multiple transform sizes into a unified context modeling framework. By merging contexts across 4×4, 8×8, 16×16 and other transforms through the context sharing map, the system achieves better probability estimation with limited context templates, as each shared context accumulates statistical information from multiple transform types.
Data Source
AI summary
Methods and apparatus are provided for unified significance map coding. An apparatus includes a video encoder (400) for encoding transform coefficients for at least a portion of a picture. The transform coefficients are obtained using a plurality of transforms. One or more context sharing maps are generated for the transform coefficients based on a unified rule. The one or more context sharing maps are for providing at least one context that is shared among at least some of the transform coefficients obtained from at least two different ones of the plurality of transforms.