Entropy Encoding Context Model Sharing for Hardware Complexity Reduction
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Solution Overview
Problem
Existing entropy-encoding and entropy-decoding methods, such as those used in MPEG-4 H.264/AVC, require frequent memory access and large memory capacity due to the need to continuously refer to previously encoded blocks for context modeling, leading to inefficiencies in entropy-encoding and decoding processes.
Innovation Solution
The method involves grouping blocks into context units and sharing a context model between adjacent blocks, where the context index for entropy-encoding and entropy-decoding is set based on a previously encoded block, reducing the need for repeated context modeling and allowing for efficient entropy-encoding and decoding by using context-based adaptive binary arithmetic coding (CABAC) or context-based adaptive variable length coding (CAVLC).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If context-based adaptive entropy-encoding/decoding is performed by frequently referring to previously encoded blocks, then encoding accuracy and compression ratio are improved, but memory access frequency and hardware complexity increase
Solution Approach 1:
The image is divided into multiple blocks that are processed in sequence. The encoding process is segmented into stages where only the current block and its immediate neighbors require context access, rather than maintaining access to all previously encoded blocks. This segmentation reduces memory access requirements while preserving the benefits of context-based adaptive coding.
Solution Approach 2:
The patent extracts and utilizes only the necessary context information from previously encoded blocks—specifically, the immediate neighboring blocks—rather than accessing the entire history of encoded blocks. This extraction approach maintains encoding accuracy by preserving relevant context while reducing memory access frequency and hardware complexity.
2Manufacturing precision
If context modeling continuously refers to previously encoded blocks, then compression ratio is improved, but memory capacity requirements increase
Solution Approach 1:
The patent applies local quality by using different context modeling approaches for different blocks based on their position and characteristics. Each block utilizes context information locally from its immediate neighbors rather than globally from all previous blocks, reducing overall memory capacity requirements while maintaining compression efficiency through localized adaptive coding.
3Manufacturing precision
If frequent memory access is performed for context modeling, then encoding precision is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary actions by pre-organizing block processing in a systematic sequence and pre-establishing context relationships between neighboring blocks. This allows the encoding process to proceed with minimal random memory access, improving processing speed while maintaining encoding precision through the pre-planned access pattern.
Data Source
AI summary
Entropy encoding and entropy decoding of image data are respectively performed whereby context modeling is performed on a context unit of blocks of the image data based on a context model of a previously encoded or decoded block.