Entropy Encoding Apparatus Using Parallel Context Modeling
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Solution Overview
Problem
Existing entropy-encoding and entropy-decoding methods, such as those used in codecs like MPEG-4 H.264/AVC, require frequent memory access and high complexity, leading to inefficiencies in image compression and decoding processes.
Innovation Solution
The proposed apparatus employs hierarchical coding units with adaptive division shapes and depths, using context modeling to optimize entropy-encoding and decoding, reducing memory access and complexity while maintaining high compression efficiency by grouping blocks into context units for shared context indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If context-based adaptive entropy-encoding/decoding (CABAC) is used, then compression efficiency is improved, but hardware complexity and memory access requirements increase
Solution Approach 1:
The patent divides the image data into multiple blocks and processes them in parallel using multiple entropy encoding/decoding units. Each unit handles a specific block independently, allowing simultaneous processing that reduces overall hardware complexity while maintaining high compression efficiency through parallel operation.
Solution Approach 2:
The patent transitions from sequential processing to parallel processing by adding a temporal dimension to the encoding/decoding process. Multiple entropy encoding/decoding units operate simultaneously on different blocks, effectively moving from a single-threaded to a multi-threaded approach that reduces hardware complexity while maintaining compression performance.
2Loss of information
If context-based adaptive entropy-encoding/decoding (CABAC) is used, then compression efficiency is improved, but memory access frequency increases
Solution Approach 1:
By segmenting the image into multiple blocks and assigning each to a dedicated entropy encoding/decoding unit, the patent reduces the frequency of memory access for context model updates. Each unit maintains its own context model locally, eliminating the need for frequent global memory access that would occur in a sequential CABAC implementation.
Solution Approach 2:
The patent creates multiple copies of the entropy encoding/decoding functionality, with each unit having its own context model and processing capabilities. This copying approach allows parallel processing without requiring frequent access to a single centralized memory location, thereby reducing memory access frequency while maintaining compression efficiency.
3Adaptability or versatility
If hierarchical coding units with adaptive division are used, then encoding flexibility is improved, but processing complexity increases
Solution Approach 1:
The patent implements dynamic block size adaptation where each entropy encoding/decoding unit can process blocks of different sizes based on the specific requirements of the image content. This dynamic approach allows the system to adapt to varying image characteristics while maintaining manageable processing complexity through the parallel architecture that distributes the adaptive processing across multiple units.
Data Source
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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.