Spatial Statistical Modeling for Low-Memory Data Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing techniques face inefficiencies in modeling large sequences of data, analyzing patterns, reducing redundancy, and lowering average 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 improve compression ratios, then memory resources are depleted when the index or dictionary becomes too large
Solution Approach 1:
The patent extracts only the essential statistical properties needed for compression from the full adaptive model, separating the critical probability information from the complete state space. This allows maintaining compression effectiveness while using minimal memory to store only the necessary statistical parameters rather than the entire index or dictionary.
Solution Approach 2:
Instead of building a large index/dictionary and then trying to compress it, the patent inverts the approach by directly modeling the statistical properties of the data source and using only those essential statistics for compression. This eliminates the need to store the full index structure in memory while achieving similar or better compression ratios.
2Measurement precision
If the number of patterns in the index or dictionary is increased to improve model accuracy, then memory resources are depleted
Solution Approach 1:
The patent extracts only the essential statistical properties needed for compression from the full adaptive model, separating the critical probability information from the complete state space. This allows maintaining compression effectiveness while using minimal memory to store only the necessary statistical parameters rather than the entire index or dictionary.
Solution Approach 2:
The patent applies partial action by using only the essential statistical properties needed for compression rather than the complete set of all possible patterns. This partial modeling approach achieves sufficient accuracy for compression purposes without the memory cost of representing all possible states, applying just enough complexity to solve the problem effectively.
3Measurement precision
If adaptive modeling is used to model more patterns, then more calculations are required to update the index or dictionary
Solution Approach 1:
The patent extracts only the essential statistical properties needed for compression from the full adaptive model, separating the critical probability information from the complete state space. This allows maintaining compression effectiveness while using minimal memory to store only the necessary statistical parameters rather than the entire index or dictionary.
Solution Approach 2:
The patent applies partial action by using only the essential statistical properties needed for compression rather than the complete set of all possible patterns. This partial modeling approach achieves sufficient accuracy for compression purposes without the memory cost of representing all possible states, applying just enough complexity to solve the problem effectively.
4Loss of information
If adaptive compression is used to compress data, then productivity is slowed due to continuous updates required
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing only the essential statistical properties of the data source before compression begins. This eliminates the need for continuous updates during encoding/decoding operations, as the model is established in advance with the necessary statistical information, significantly improving processing speed while maintaining compression effectiveness.
Solution Approach 2:
The patent extracts only the essential statistical properties needed for compression from the full adaptive model, separating the critical probability information from the complete state space. This allows maintaining compression effectiveness while using minimal memory to store only the necessary statistical parameters rather than the entire index or dictionary.
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.