Transform Coefficient Context Coding for Lower Video Bandwidth
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Solution Overview
Problem
Current video coding techniques face inefficiencies in determining contexts for entropy coding, particularly in handling transform coefficients, which affects compression efficiency and bandwidth usage.
Innovation Solution
The proposed solution involves determining contexts for entropy coding based on regions within a video block, using position-based or neighborhood-based context information, and applying context offsets to optimize the coding of transform coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy coding methods are used for transform coefficients, then the coding process is simple, but compression efficiency is insufficient and bandwidth consumption is high
Solution Approach 1:
The patent divides the transform coefficient block into multiple regions (e.g., top-left region, bottom-right region, and other regions) and assigns different context models to different regions. This allows the coding process to adapt to the local characteristics of transform coefficients in different areas, improving compression efficiency by capturing spatial variations in coefficient distributions without requiring a completely complex system overhaul.
Solution Approach 2:
The patent segments the transform coefficient coding process by separating it into different context-adaptive coding paths based on region. Instead of using a single uniform coding approach for all coefficients, the method segments the coefficient block and applies specialized context models to specific segments, thereby improving overall compression while maintaining manageable complexity through modular region-based processing.
2Productivity
If region-based context determination is implemented, then coding efficiency improves, but processing complexity increases
Solution Approach 1:
By implementing region-based context determination, the patent applies different context models to different spatial regions of the transform coefficient block. This local quality approach improves coding efficiency by matching context models to regional characteristics while keeping the overall system complexity manageable through systematic region classification rather than individual coefficient processing.
Solution Approach 2:
The patent performs preliminary classification of transform coefficients into different regions before applying context-adaptive coding. This preliminary action organizes the coding process by pre-establishing region boundaries and assigning context models in advance, which reduces processing complexity during the actual coding phase by avoiding real-time complex decisions for each coefficient.
3Loss of energy
If context offsets are applied to optimize coding, then bandwidth consumption decreases, but computational requirements increase
Solution Approach 1:
The patent applies context offsets to adjust the context model parameters based on regional characteristics and coefficient properties. This parameter change approach optimizes the coding process by dynamically adjusting context values to better match actual coefficient distributions, thereby reducing the number of bits needed for encoding (lower bandwidth consumption) while using systematic parameter adjustment rules that control computational overhead.
Data Source
AI summary
In one example, a device for coding video data includes a video coder configured to determine values for coded sub-block flags of one or more neighboring sub-blocks to a current sub-block, determine a context for coding a transform coefficient of the current sub-block based on the values for the coded sub-block flags, and entropy code the transform coefficient using the determined context.