Combinatorial Block Coding for Variable-Probability Data Compression
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Solution Overview
Problem
Current data compression methods, such as arithmetic coding and Golomb coding, are inefficient for compressing data with varying symbol probabilities, leading to suboptimal compression performance, especially when the probability of a symbol is between 0.2 and 0.5, and may result in increased computational costs due to large pattern numbers.
Innovation Solution
The Combinatorial Coding technique partitions data into manageable blocks, generates symbol string codes indicative of the number and pattern of occurrences, and combines these codes to achieve efficient compression, utilizing combinatorial mathematics to reduce the number of bits required for encoding, particularly effective for binary data with constant probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If arithmetic coding or Golomb coding is used for data compression, then compression is achieved, but compression performance becomes suboptimal when symbol probability is between 0.2 and 0.5
Solution Approach 1:
The patent changes the coding parameters by using combinatorial number system with block size k and symbol probability p as variables. The code length is dynamically adjusted based on the actual symbol probability observed in the data, allowing optimal compression for any probability distribution rather than being fixed for specific probability ranges.
Solution Approach 2:
The invention introduces dynamic adaptation by estimating symbol probability from the input data and adjusting the combinatorial coding parameters accordingly. The block size k and coding scheme are selected based on the actual data characteristics, making the compression method adaptive rather than static.
2Measurement precision
If combinatorial coding with large pattern numbers is used, then compression is achieved, but computational cost increases
Solution Approach 1:
The patent segments the data into blocks of size k and uses combinatorial numbering to represent patterns within each block. By dividing the compression task into manageable blocks and using efficient combinatorial indexing, the computational complexity is reduced from exponential to polynomial in the block size.
Solution Approach 2:
The invention uses pre-computed combinatorial number tables and patterns that can be reused across different data blocks. The combinatorial coding scheme relies on replicated pattern structures that can be efficiently generated and stored, reducing repeated computational overhead.
Data Source
AI summary
Embodiments described herein may include example embodiments of a method, article and apparatus for compressing data utilizing combinatorial coding which may be used for communicating between two or more components connected to an interconnection medium (e.g., a bus) within a single computer or digital data processing system, and/or for communication between computing platforms via a network or other interconnection medium.