Spatial State Subset Modeling for Low-Memory Data Compression
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Solution Overview
Problem
Current data processing techniques face inefficiencies in modeling large sequences of data, analyzing patterns, determining probabilities, and reducing entropy without loss, particularly due to high memory and computational costs, and the inability to efficiently compress random-like data.
Innovation Solution
The method identifies a subset of states within a data system using a spatial statistical model that represents systemic characteristics and relationships, allowing for efficient modeling and compression by creating identifiers that correspond to probable states, reducing memory and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If adaptive modeling is used to model more patterns, then compression ratio is improved, but memory resources are depleted and computational cost increases
Solution Approach 1:
The patent extracts only the essential probability information needed for compression by using arithmetic coding to represent probability distributions compactly. Instead of storing complete adaptive models or dictionaries, the system extracts and transmits only the necessary probability parameters, significantly reducing memory requirements while maintaining compression effectiveness.
Solution Approach 2:
The patent transforms the representation of probability distributions from discrete frequency counts into continuous probability space using arithmetic coding. This dimensional transformation allows the system to model complex patterns with continuous probability values rather than discrete state frequencies, reducing the memory footprint from exponential to polynomial complexity.
2Loss of information
If adaptive modeling is used to model more patterns, then compression ratio is improved, but computational cost increases
Solution Approach 1:
The patent replaces the mechanical process of updating and querying large adaptive dictionaries or Huffman trees with arithmetic coding operations. Instead of traversing complex data structures to encode/decode symbols, the system uses straightforward arithmetic operations on probability intervals, dramatically reducing computational complexity from O(n) to O(1) per symbol.
Solution Approach 2:
The patent changes the parameters used for modeling from discrete frequency counts to continuous probability distributions. This parameter transformation allows the system to achieve high compression ratios by accurately modeling symbol probabilities without requiring large amounts of training data or complex adaptive structures, reducing both computational and memory requirements.
3Device complexity
If static index or dictionary is used, then computational cost is reduced, but compression ratio deteriorates when number of patterns is large
Solution Approach 1:
The patent introduces dynamics into the probability modeling process by allowing probability distributions to adapt to the input data characteristics. Using arithmetic coding, the system dynamically adjusts probability estimates based on observed frequencies while maintaining a compact representation. This dynamic adaptation achieves high compression ratios for large pattern sets without requiring large static dictionaries or indexes.
Data Source
AI summary
A method, article comprising machine-readable instructions and apparatus that processes data systems for encoding, decoding, pattern recognition/matching and data generation is disclosed. State subsets of a data system are identified for the efficient processing of data based, at least in part, on the data system's systemic characteristics.