Hybrid Data Compression Selection for Mixed Data Blocks
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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, which do not guarantee robust compressibility across varying data types and value locality properties, leading to increased latency and complexity.
Innovation Solution
The implementation of hybrid data compression systems that combine multiple compression methods, using a predictor to select the best suited compression scheme based on the dominating data type of a block, allowing for dynamic adjustment at runtime if necessary, to optimize compressibility and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single compression scheme is used, then device complexity is reduced, but compressibility and performance across varying data types deteriorate
Solution Approach 1:
The patent implements a dynamic compression system that switches between different compression schemes (e.g., Huffman, LZW, arithmetic coding) based on the characteristics of the input data. A data type detector analyzes the incoming data stream and selects the most appropriate compression algorithm in real-time, allowing the system to adapt to varying data types and achieve optimal compressibility without being locked into a single static scheme.
Solution Approach 2:
The system changes operational parameters by adjusting which compression algorithm is active based on detected data characteristics. When specific data patterns are identified (e.g., high redundancy, specific data types), the system transitions between compression schemes, effectively changing the compression parameter set to match the data being processed, thereby improving overall compressibility across diverse data types.
2Adaptability or versatility
If multiple compression schemes are combined, then compressibility is improved, but latency increases due to selection overhead
Solution Approach 1:
The patent implements a data type detector that performs preliminary analysis of the incoming data stream before compression begins. By detecting data characteristics and pre-selecting the appropriate compression scheme in advance, the system avoids runtime decision-making delays during the actual compression process, thereby reducing latency while still benefiting from multiple compression options.
Solution Approach 2:
The compression system processes data in segments or blocks, applying the same compression scheme consistently to each segment based on its characteristics. This segmentation approach allows the detector to make selection decisions at manageable intervals rather than continuously, reducing the frequency of scheme switching and associated overhead, thereby lowering latency while maintaining adaptability.
3Adaptability or versatility
If multiple compression schemes are combined, then compressibility is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal compression framework that can accommodate multiple compression schemes through a common architecture. The data type detector and scheme selector serve as universal components that work with various compression algorithms (Huffman, LZW, arithmetic coding, etc.), allowing the system to support multiple schemes without requiring separate independent systems for each, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The patent introduces intermediary components (data type detector, scheme selector, and controller) that mediate between the input data and the multiple compression schemes. These intermediaries simplify the interface between data and various compression algorithms, managing the complexity of coordinating multiple schemes while enabling the system to leverage their combined compressibility benefits through centralized control logic.
Data Source
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.