Combinatorial Coding Blocks for Adaptive Data Compression
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Solution Overview
Problem
Current data compression methods are inefficient in reducing bandwidth utilization during data transmission and storage, particularly when dealing with large datasets, as they often fail to effectively compress data sequences with varying patterns and probabilities.
Innovation Solution
The implementation of a combinatorial coding scheme that partitions data into manageable blocks, generates symbol string codes indicating the number and pattern of occurrences, and combines these codes to form a compressed data code, utilizing techniques like Huffman and Golomb coding to optimize compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If current data compression methods are used, then data transmission and storage are performed, but bandwidth utilization is not effectively reduced and compression efficiency is poor
Solution Approach 1:
The patent segments data into blocks and further divides each block into sub-blocks, allowing independent coding of each sub-block. This segmentation enables the system to adapt to local variations in data patterns and probability distributions, achieving better compression efficiency and reduced bandwidth utilization compared to treating the entire data set as a single unit.
Solution Approach 2:
The patent employs dynamic probability distribution modeling where the probability distribution is updated for each sub-block based on observed data characteristics. This dynamic adaptation allows the compression scheme to respond to changing patterns in the data, improving compression ratios and reducing bandwidth requirements while maintaining high compression efficiency.
2Quantity of substance
If data is compressed using traditional methods, then some compression is achieved, but compression rates do not approach the theoretical minimum
Solution Approach 1:
The patent changes the parameter of probability distribution representation by using adaptive models that adjust to local data characteristics. By modifying how probability distributions are estimated and applied to different sub-blocks, the system achieves compression rates that approach the theoretical minimum, significantly reducing the number of bits required to represent the data.
3Adaptability or versatility
If compression schemes are made adaptable to changing patterns, then compression efficiency improves, but system complexity increases
Solution Approach 1:
By segmenting data into smaller sub-blocks, the patent reduces the complexity of adapting to changing patterns within each segment while maintaining overall adaptability. Each sub-block can be processed independently with simpler probability models, avoiding the need for complex global adaptation mechanisms.
Solution Approach 2:
The patent applies partial adaptation by focusing computational effort on modeling the probability distribution for each sub-block independently rather than attempting to model the entire data set. This partial action approach achieves sufficient adaptability for compression while keeping the overall system complexity manageable.
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.