Unified Significance Map Coding Across Multiple Transform Sizes
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Solution Overview
Problem
Existing video coding standards fail to effectively capture the differences in coefficient distributions across various transform sizes, leading to inefficient context modeling and increased complexity in encoding and decoding significance maps.
Innovation Solution
A unified rule-based approach is introduced to generate context sharing maps for transform coefficients, adapting to the transform size and simplifying the design by using a consistent method across different transforms, reducing the number of context models while maintaining flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate context models are used for different transform sizes, then the coding can capture differences in coefficient distributions, but the device complexity and number of context models increase
Solution Approach 1:
The patent applies universality by creating a unified context model that serves multiple transform sizes (4×4, 8×8, 16×16, 32×32) simultaneously. Instead of maintaining separate context models for each transform size, a single context model is designed to adaptively handle different transform types, reducing the overall number of context models while maintaining coding precision through adaptive probability updates based on transform-specific characteristics
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the context model parameters (probability values) based on the transform size and previously decoded coefficients. The context model adapts its parameters according to the specific transform being used, allowing a single model to effectively capture the different coefficient distributions of various transform sizes without requiring separate models
2Productivity
If more context models are used to capture coefficient distribution differences, then coding efficiency improves, but the encoding and decoding complexity increases
Solution Approach 1:
The unified context model performs multiple functions by handling different transform sizes within a single model framework, reducing the computational overhead of switching between multiple specialized models while maintaining the ability to adapt to different coefficient distributions through parameter adjustments
Solution Approach 2:
The patent merges the functionality of multiple transform-specific context models into a single unified model. By combining the context modeling for 4×4, 8×8, 16×16, and 32×32 transforms into one model, the patent reduces encoding complexity while preserving the ability to capture transform-specific characteristics through adaptive probability updates
3Adaptability or versatility
If transform-specific context models are implemented, then adaptability to different transforms improves, but the number of context models and design complexity increase
Solution Approach 1:
The unified context model achieves transform adaptability through multi-functionality, where a single model structure is designed to handle multiple transform types by adapting its internal parameters based on the transform size and characteristics, eliminating the need for separate dedicated models for each transform size
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.