Unified Significance Map Coding Across Multiple Transform Sizes
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Solution Overview
Problem
Existing video coding standards, such as MPEG-4 AVC and KTA, face challenges in efficiently coding significance maps for transform coefficients across different transform sizes, leading to inadequate capture of coefficient distributions and complexity 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 transform sizes, allowing multiple transform coefficient positions to share contexts, simplifying encoder and decoder design and enabling easy extension to future standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate context models are used for different transform sizes, then the capture of coefficient distributions is improved, but the device complexity and number of context models increases
Solution Approach 1:
The patent applies universality by creating a unified context model that serves multiple transform sizes (4×4, 8×8, 16×16, 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 types, reducing the total number of context models while maintaining effective capture of coefficient distributions across all transform sizes.
2Productivity
If separate designs are created for each transform size, then the coding efficiency for that transform is improved, but the ease of extending to future standards deteriorates
Solution Approach 1:
The patent applies dynamics by designing a context model with adaptive parameters that can be configured for different transform sizes. The context model includes transform-size-dependent parameters that allow it to dynamically adjust its behavior based on the specific transform type being used, maintaining high coding efficiency for current transforms while enabling easy extension to future transform types through parameter reconfiguration rather than requiring new context models.
3Measurement precision
If more context models are used to accurately represent different transform coefficient patterns, then the measurement precision of coefficient distributions is improved, but the loss of time for model initialization and selection increases
Solution Approach 1:
The patent applies merging by consolidating multiple transform-size-specific context models into a single unified context model. This unified model incorporates transform size as one of its parameters, allowing it to represent coefficient distributions for all transform sizes (4×4, 8×8, 16×16, and future transforms) within one model structure. This eliminates the need for separate model initialization and selection processes for each transform size, reducing time loss while maintaining accurate representation of coefficient distributions.
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.