Parallel Context Processing for HEVC Entropy Coding
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Solution Overview
Problem
Existing video coding standards, such as AVC, face challenges in decoding high bit rate video streams in real-time due to the serial nature of context-adaptive binary arithmetic coding (CABAC), which leads to high power consumption and potential feasibility issues.
Innovation Solution
The proposed solution involves parallel context processing techniques for high coding efficiency entropy coding in HEVC, which includes retrieving syntax elements, grouping bins based on similar contexts, and coding these grouped bins in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If CABAC is used for entropy coding, then coding efficiency is improved, but power consumption increases and real-time decoding becomes difficult
Solution Approach 1:
The patent divides the syntax elements into multiple groups (first group, second group, third group) that can be processed in parallel. This segmentation allows the CABAC engine to process different groups simultaneously rather than sequentially, improving throughput and reducing power consumption while maintaining coding efficiency.
2Manufacturing precision
If CABAC is used for entropy coding, then coding efficiency is improved, but processing speed decreases
Solution Approach 1:
The patent segments syntax elements into multiple groups that can be processed in parallel. The first group contains syntax elements processed with one context model, the second group with another context model, allowing simultaneous processing and improved throughput.
Solution Approach 2:
The patent introduces parallel processing dimensions by creating multiple context models (first context model, second context model) that operate simultaneously on different groups of syntax elements, transforming the single-threaded serial process into a multi-threaded parallel process.
3Device complexity
If serial context processing is used, then implementation is simpler, but throughput is limited
Solution Approach 1:
The patent divides syntax elements into distinct groups (first group, second group, third group) that can be processed independently in parallel. This segmentation enables higher throughput by eliminating serial dependencies while keeping each group's processing relatively simple.
Solution Approach 2:
The patent adds parallel processing capability by introducing multiple context models that operate simultaneously on different syntax element groups, transforming the single-dimensional serial processing into multi-dimensional parallel processing.
Data Source
AI summary
A method and apparatus for parallel context processing for example for high coding efficient entropy coding in HEVC. The method comprising retrieving syntax element relating to a block of an image, grouping at least two bins belonging to similar context based on the syntax element, and coding the grouped bins in parallel.