Context Sharing Maps for Significance Coding Across Transform Sizes
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Solution Overview
Problem
Existing video coding standards, such as MPEG-4 AVC and KTA software, 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 proposed for generating context sharing maps that adapt to different transform sizes, allowing multiple transform coefficient positions to share contexts, simplifying the design of both 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 distributions can be accurately captured, but the device complexity and number of context models increase significantly
Solution Approach 1:
The patent applies universality by creating a unified context model that serves multiple transform sizes (4x4, 8x8, 16x16, and future transform types). Instead of having separate context models for each transform size, a single context model is designed to adaptively handle different transform sizes through unified significance map coding rules, reducing the number of context models while maintaining coding efficiency
Solution Approach 2:
The patent uses parameter changes by modifying the context model's behavior based on transform size parameters. The unified context model adjusts its coding characteristics dynamically according to the transform size being processed, allowing one context model to effectively capture coefficient distributions across multiple transform sizes without requiring separate models for each
2Productivity
If separate context modeling designs are created for each transform size, then the coding efficiency is optimized for each transform, but the ease of extension to future standards is reduced
Solution Approach 1:
The unified context model design provides universality by creating a single adaptable framework that can handle current transform sizes and easily extend to future transform types. The standardized coding rules and unified significance map approach allow the same context model to be applied across different transform sizes without requiring separate design efforts for each new transform type
Solution Approach 2:
The patent applies dynamics by designing a flexible context model that can adapt its behavior based on the specific transform size being processed. The unified significance map coding rules dynamically adjust the context model's operation to match the characteristics of different transform sizes, enabling both optimized coding efficiency and easy extendability to future standards
3Device complexity
If limited context templates are used, then the model cost is reduced, but the measurement precision of conditional probability estimates decreases
Solution Approach 1:
The patent uses parameter changes by modifying how context templates are utilized within the unified context model. Instead of increasing the number of context templates, the system changes the parameter of template utilization efficiency by employing standardized significance map coding rules that extract maximum information from limited templates, thereby maintaining accurate conditional probability estimates with reduced model cost
Data Source
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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 plurali- ty 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.