Entropy Encoder Transform Coefficient Encoding
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Solution Overview
Problem
Conventional image compression algorithms face a trade-off between efficiency and complexity in encoding transform coefficients, lacking a method that balances high efficiency with low complexity.
Innovation Solution
The proposed solution involves an entropy encoder that encodes each transform coefficient as a symbol comprising a context, magnitude, and mantissa, using a subset of Huffman codes determined by the context, to efficiently encode and decode transform coefficients in image compression systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional encoding methods are used to encode transform coefficients, then encoding efficiency is improved, but computational complexity increases
Solution Approach 1:
The transform coefficients are segmented into four different contexts based on their magnitude ranges (e.g., zero, small magnitude, medium magnitude, large magnitude). Each context is encoded using a dedicated subset of Huffman codes, allowing the encoding process to be divided into manageable segments that can be processed independently with optimized code tables.
Solution Approach 2:
Instead of using the complete Huffman code set for all coefficients, the invention employs partial Huffman code subsets tailored to specific coefficient contexts. This partial action approach reduces the search space and computational overhead while maintaining high encoding efficiency for each coefficient type.
2Speed
If conventional encoding methods are used to encode transform coefficients, then encoding speed is improved, but computational complexity increases
Solution Approach 1:
Huffman code subsets are pre-computed and stored for each of the four contexts before the actual encoding process. During encoding, the system only needs to lookup the appropriate pre-prepared code subset based on the coefficient's context, significantly speeding up the encoding process while reducing real-time computational complexity.
Solution Approach 2:
Different Huffman code subsets are assigned to different coefficient contexts based on their local characteristics (magnitude ranges). This local optimization ensures that each context uses the most appropriate code table for its specific data distribution, improving encoding speed without requiring a single complex universal code table.
Data Source
AI summary
Various embodiments of the present technology may comprise methods and apparatus for transform coefficient encoding and decoding. Methods and apparatus for transform coefficient encoding and decoding may comprise an entropy encoder configured to encode each transform coefficient as a symbol, wherein the symbol comprises a context, a magnitude, and a mantissa. The context may be one of four contexts and the symbol may be encoded using a subset of Huffman codes, wherein the subset is determined based on the context.