Symbol Compression via Conditional Entropy Estimation
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Solution Overview
Problem
Existing digital content compression techniques, such as JBIG2, face inefficiencies in dictionary construction and symbol encoding, leading to suboptimal compression ratios and increased storage and transmission bandwidth due to reliance on empirical threshold values and similarity-based clustering methods.
Innovation Solution
The use of conditional entropy estimation to determine the number of bits needed to encode symbols using their associated dictionary entries, optimizing dictionary construction and clustering to minimize distortion and improve compression ratios, while maintaining compatibility with JBIG2 standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If empirical threshold values and similarity-based clustering methods are used for dictionary construction, then the implementation is simpler, but the compression ratio is suboptimal
Solution Approach 1:
The patent changes the fundamental parameter used for dictionary construction from empirical threshold values to conditional entropy estimation. This allows the system to optimize compression ratios by accurately determining the number of bits needed to encode symbols, while maintaining computational feasibility through efficient entropy estimation algorithms.
Solution Approach 2:
The patent replaces the mechanical similarity-based clustering approach with an information-theoretic conditional entropy estimation method. This substitution enables more accurate bit rate estimation and optimal dictionary construction, improving compression efficiency without excessive computational overhead.
2Measurement precision
If more bits are used to encode symbols, then the encoding precision is higher, but the file size increases
Solution Approach 1:
The patent employs conditional entropy estimation as a feedback mechanism to determine the optimal number of bits for encoding symbols. By estimating the information content of symbols relative to dictionary entries, the system dynamically allocates bit rates, ensuring sufficient encoding precision while minimizing file size through efficient resource allocation.
3Productivity
If dictionary construction is optimized for better compression, then the compression ratio improves, but the computational complexity increases
Solution Approach 1:
The patent performs conditional entropy estimation and dictionary construction as preliminary actions before the actual encoding process. By pre-optimizing the dictionary based on estimated symbol probabilities, the system achieves high compression ratios during encoding without excessive computational complexity during the actual compression operation.
Data Source
AI summary
The present disclosure includes a system and method for symbol compression using conditional entropy estimation. One method for symbol compression using conditional entropy estimation includes approximating a quantity of symbol encoding bits for a number of symbols using a conditional entropy estimation. Dictionary entries are generated from the number of symbols so as to minimize a total bit-stream quantity. The total bit-stream quantity includes at least the approximated quantity of symbol encoding bits and a quantity of dictionary entries encoding bits. The symbols are encoded using the dictionary entries as a reference.