Hybrid Data Compression Using Data Type Prediction
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Solution Overview
Problem
Current data compression and decompression methods in computer systems and communication networks face inefficiencies due to reliance on single compression schemes that do not consistently perform well across various data types, leading to unpredictable compressibility and increased latency.
Innovation Solution
The implementation of hybrid data compression and decompression systems that combine multiple compression methods, using a predictor to select the best suited scheme based on the dominating data type within a data block, allowing for dynamic adjustment at runtime if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single compression scheme is used, then the device complexity is reduced, but the compressibility becomes unpredictable and performance varies across different data types
Solution Approach 1:
The compression system is designed to support multiple compression schemes (e.g., Huffman coding, arithmetic coding, run-length encoding) within a single device. A predictor mechanism determines which scheme to apply based on the data type, allowing the system to adapt to different data characteristics while maintaining a unified architecture. This multi-functional approach resolves the contradiction by enabling high compressibility across various data types without requiring separate dedicated devices for each scheme.
Solution Approach 2:
The system dynamically selects the appropriate compression scheme at runtime based on the predicted data type. The predictor analyzes incoming data characteristics and adjusts the compression method accordingly, rather than using a fixed single scheme. This dynamic adaptation allows the system to optimize compressibility for each data block while managing complexity through intelligent control rather than hardware multiplication.
2Adaptability or versatility
If multiple compression schemes are combined, then the compressibility improves, but the latency increases due to prediction and selection overhead
Solution Approach 1:
The predictor mechanism performs data type prediction in advance, before the actual compression process begins. By analyzing data characteristics upfront and determining the optimal compression scheme beforehand, the system avoids trial-and-error approaches and reduces the time penalty associated with scheme selection. This preliminary action ensures that the most suitable compression method is ready to be applied immediately, minimizing overall latency while maintaining high compressibility.
3Measurement precision
If data types are predicted accurately, then the best compression method can be selected, but the prediction process adds complexity to the system
Solution Approach 1:
The predictor mechanism focuses on identifying specific data type characteristics (such as integer ranges, floating-point patterns, or structured data formats) rather than attempting to analyze all possible data properties. By concentrating prediction efforts on the most discriminative local features of data types, the system achieves high prediction accuracy without requiring complex omnibus analysis mechanisms. This selective approach to prediction maintains precision while controlling system complexity.
Data Source
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AI summary
Methods, devices and systems enhance compression and decompression of data blocks of data values by selecting the best suited compression method and device among two or a plurality of compression methods and devices, which are combined together and which said compression methods and devices compress effectively data values of particular data types; said best suited compression method and device is selected using as main selection criterion the dominating data type in a data block by predicting the data types within said data block.