Hybrid Data Compression Using Segment-Wise Algorithm Selection
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Solution Overview
Problem
Current data compression algorithms lack the ability to dynamically select the optimal compression method based on the specific characteristics of the data, leading to suboptimal compression ratios and efficiency in data storage and transmission.
Innovation Solution
An information handling system that processes a training data file to determine an optimal data compression algorithm by performing a compression ratio analysis and probability analysis, generating symbol transition matrices and probability matrices to predict the best compression algorithm for each segment of a working data file, allowing for content-based dynamic hybrid data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single data compression algorithm is used for all data files, then the system complexity is reduced and ease of operation is improved, but the compression ratio and storage efficiency deteriorate
Solution Approach 1:
The system dynamically selects compression algorithms based on the characteristics of each data file. A probability analysis engine evaluates the data and determines the optimal compression algorithm to apply, allowing the system to adapt to different data types and patterns rather than using a static, single algorithm for all files.
Solution Approach 2:
The system changes the parameter of algorithm selection based on data characteristics. By analyzing probability distributions and statistical features of the input data, the system selects from multiple compression algorithms (e.g., Huffman, LZW, arithmetic coding) the one that best matches the data pattern, thereby optimizing compression ratio.
2Loss of substance
If multiple data compression algorithms are evaluated and selected based on probability analysis, then the compression ratio is improved, but the computational complexity and processing time increase
Solution Approach 1:
The compression process is segmented into distinct phases: a training phase where probability models are built from sample data, and an execution phase where pre-computed probability matrices are used for rapid algorithm selection. This segmentation allows complex probability analysis to be performed once during training, rather than repeatedly during actual compression operations.
Solution Approach 2:
Probability matrices and statistical parameters are computed in advance during a training phase using representative data samples. These pre-computed probability models are then stored and reused during actual compression operations, eliminating the need to perform complex probability analysis in real-time and significantly reducing computational overhead.
3Measurement precision
If probability matrices and symbol transition matrices are generated through training data analysis, then the accuracy of algorithm selection is improved, but the processing time and computational resources increase
Solution Approach 1:
Probability matrices and symbol transition matrices are generated in advance during a training phase using representative data samples. These probability models are stored and reused during actual compression operations, eliminating the need to perform complex probability analysis in real-time and significantly reducing computational overhead.
Solution Approach 2:
The system uses a limited set of pre-computed probability matrices derived from training data to make algorithm selection decisions. Rather than performing exhaustive analysis on every data file, the system applies these pre-established probability models to quickly determine the optimal compression algorithm, achieving high accuracy without excessive processing time.
Data Source
AI summary
An information handling system includes a processor configured to process a training data file to determine an optimal data compression algorithm. The processor may also perform a compression ratio analysis that includes compressing the training data file using data compression algorithms, calculating a compression ratio associated with each of the data compression algorithms, determining an optimal compression ratio from the compression ratio associated with the each data compression algorithm; and determining a desirable data compression algorithm associated with the training data file based on the optimal compression ratio. The processor may also perform a probability analysis that includes generating a symbol transition matrix based on the desirable data compression algorithm, extracting statistical feature data based on the symbol transition matrix, and generating probability matrices based on the statistical feature data to determine the optimal data compression algorithm for each segment of a working data file.