Spatial Probability Model for 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 computational and memory costs, and inability to efficiently handle 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 encoding of data sequences with reduced memory and processing costs, and enables encoding and decoding of random-like data.
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 probability information needed for compression by using a probability gradient function that models the distribution of data values. Instead of storing complete adaptive indexes or dictionaries, the system extracts and stores only the gradient parameters that characterize the probability distribution, dramatically reducing memory requirements while maintaining compression effectiveness.
Solution Approach 2:
The patent changes the parameter representation from storing complete probability tables or dictionaries to storing gradient function parameters. By representing the probability distribution through a continuous gradient function with a limited set of parameters, the system achieves both high compression ratios and low memory consumption, resolving the contradiction between compression quality and memory usage.
2Loss of information
If adaptive modeling is used to improve compression ratios, then computational cost increases due to constant model updates
Solution Approach 1:
The patent performs preliminary action by establishing a probability gradient model that can be updated efficiently. Instead of constantly updating complete probability tables during decoding, the system pre-establishes the gradient function structure and updates only the gradient parameters, which are far fewer in number. This preliminary setup reduces the computational burden during actual encoding and decoding operations.
Solution Approach 2:
The patent applies partial action by updating only the essential gradient parameters rather than complete probability distributions. The gradient function approach allows the system to capture the essential probability information with partial updates to a limited set of parameters, significantly reducing computational cost compared to updating full adaptive models while maintaining adequate compression performance.
3Measurement precision
If the number of patterns in the index or dictionary is increased to improve accuracy, then compression advantage is eliminated after appending the index
Solution Approach 1:
The patent extracts only the essential probability gradient information needed for accurate modeling, rather than storing complete indexes or dictionaries. By extracting and storing only the gradient parameters that characterize the probability distribution, the system achieves high modeling accuracy with minimal overhead, preventing the compression advantage from being eliminated.
Solution Approach 2:
The patent changes from discrete probability tables to continuous gradient function parameters. This parameter transformation allows the system to represent complex probability distributions with a compact set of continuous parameters, achieving high accuracy without the exponential growth in index size that would eliminate compression benefits.
4Power
If static indexes or dictionaries are used to reduce computational cost, then adaptability to data patterns is reduced
Solution Approach 1:
The patent introduces dynamics by using a probability gradient function that can adapt to data patterns while maintaining computational efficiency. The gradient function parameters can be updated to reflect changing data distributions, providing adaptability similar to full adaptive models but with the computational simplicity of static models. This dynamic parameter adjustment enables the system to adapt to various data patterns without the high computational cost of traditional adaptive modeling.
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.