Virtualized Data Compression for Unpredictable Lossless Data
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Solution Overview
Problem
Existing data compression techniques are inefficient for uniformly distributed or unpredictable data, often requiring high matching rates or loss of information, which is unacceptable for certain types of data like text or executable files.
Innovation Solution
The method generates a stream of numbered sequences with predetermined amplitude and length, applies error correction protocols, and uses independent component analysis to determine compression depth and execute a specified compression routine, eliminating the need for exact dictionary matches and reducing data redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dictionary algorithms are used for data compression, then compression ratio can be improved through pattern matching, but the method fails for unpredictable data requiring high matching rates
Solution Approach 1:
The patent applies preliminary actions by performing error correction and data transformation before compression. The system identifies and corrects errors in the input data stream, then transforms the corrected data into a format more suitable for compression, thereby improving compression effectiveness for unpredictable data types
Solution Approach 2:
The patent changes parameters by transforming the data representation format and adjusting compression settings based on data analysis. The system modifies data parameters through error correction and transformation processes, then adapts compression parameters to achieve better compression ratios for different data types
2Productivity
If lossy compression techniques are applied to achieve compression, then compression efficiency is improved, but information loss occurs which is unacceptable for certain data types
Solution Approach 1:
The patent applies preliminary error correction before compression to improve compression efficiency without sacrificing data integrity. By correcting errors in advance, the system creates a cleaner data stream that compresses more efficiently while ensuring data can be perfectly reconstructed during decompression
Solution Approach 2:
The patent uses feedback mechanisms to verify data integrity throughout the compression process. The system monitors compression operations and ensures that decompression can perfectly reconstruct the original data, providing feedback to adjust compression parameters when necessary to maintain lossless compression
3Measurement precision
If exact match between sequences is required for compression, then dictionary algorithm accuracy is improved, but compression fails when matching rate is insufficient
Solution Approach 1:
The patent applies preliminary error correction to improve sequence matching accuracy before dictionary compression. By correcting errors in advance, the system increases the likelihood of finding matching sequences in the dictionary, thereby improving both matching accuracy and compression success rate
Solution Approach 2:
The patent changes data parameters through transformation processes that enhance sequence matchability. The system transforms corrected data into formats that better match dictionary entries, adjusting parameters to improve both matching precision and overall compression effectiveness
Data Source
AI summary
Systems, media, and methods for virtualized data compression are provided. For example, a stream of numbered sequences may be generated by transforming an input stream into a sequence of samples each having a predetermined amplitude and a predetermined length. An error correction protocol may be applied through an analysis of a number of bits. A compression routine may be implemented by choosing a number of threads and determining compression depth. The stream analyze may be analyzed utilizing independent component analysis. A specified compression routine may be executed. An output file size may be determined.