Hybrid Entropy Coding for Fixed-Rate Scalar Quantization
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Solution Overview
Problem
Fixed rate image compression algorithms face inefficiencies due to poor performance from scalar quantization and variable bit rate requirements, which existing entropy coding techniques like Huffman coding cannot optimally address.
Innovation Solution
Combining Huffman coding or other entropy coding techniques with a fixed length coding scheme, where fixed length codes are assigned to high-probability symbols and entropy coding is used for lower probability symbols, with a mechanism to adjust quantization step size to ensure encoding within a fixed bit packet size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If scalar quantization is used for fixed rate compression, then computational complexity is reduced compared to vector quantization, but compression performance deteriorates
Solution Approach 1:
The patent combines scalar quantization with entropy coding to achieve better compression performance while maintaining low computational complexity. The quantized symbols are then entropy coded using Huffman coding or other entropy coding techniques, creating a hybrid approach that leverages the simplicity of scalar quantization with the efficiency of entropy coding.
Solution Approach 2:
The patent adjusts the quantization step size dynamically to control the number of bits required to represent quantized symbols. By changing the quantization step size parameter, the system can adapt to different compression requirements and ensure that the entropy coded symbols fit within the fixed bit rate constraint.
2Productivity
If entropy coding is used to minimize average bit rate, then compression efficiency improves, but fixed rate constraint cannot be guaranteed
Solution Approach 1:
The patent performs quantization before entropy coding, and adjusts the quantization step size in advance to ensure that the resulting entropy coded symbols can be represented within the fixed bit rate constraint. This preliminary adjustment of quantization parameters ensures that the fixed rate constraint is met while still allowing entropy coding to improve compression efficiency.
Solution Approach 2:
The system uses feedback from the entropy coding process to adjust the quantization step size. If the entropy coded symbols require more bits than the fixed rate constraint allows, the quantization step size is increased to reduce the number of bits required, ensuring compliance with the fixed rate constraint.
3Quantity of substance
If quantization step size is increased to reduce bit count, then fixed rate constraint is satisfied, but information loss increases
Solution Approach 1:
The patent dynamically adjusts the quantization step size parameter to find the optimal balance between bit count and information loss. By changing the quantization step size, the system can control the number of bits required to represent quantized symbols while minimizing the information loss, ensuring that the fixed rate constraint is met with the least possible degradation in quality.
Data Source
AI summary
A system and method combine an entropy coding algorithm like Huffman or arithmetic coding with a fixed length coding scheme and can improve the compression performance in a fixed rate compression scheme. The fixed length code assigns codes with a fixed length to symbols that have the highest probability of occurrence. Therefore, fixed length coding is used if all symbols in the sequence are from the set of symbols that have the highest probability values. Otherwise an entropy coding algorithm (e.g. Huffman coding) is used to encode quantized symbols.