Binary Data Compression Using Root Identifiers and Multi-Cycle Schemes
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Solution Overview
Problem
Current binary data compression/decompression methods are inefficient in achieving significant compression gains due to limitations in identifying and processing content variations and distributions within arbitrary binary data strings, particularly in hardware implementations where speed and cost trade-offs are significant.
Innovation Solution
The proposed binary data compression/decompression method (BDCD) employs a fully-pipelined architecture and introduces four compression schemes that create and process complex structures using a well-defined set of root identifiers (RI) to identify and exploit content variations and distributions, allowing for multiple compression cycles and efficient hardware implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware implementation is used to achieve faster compression/decompression speed, then speed is improved, but cost increases
Solution Approach 1:
The compression system is divided into distinct functional modules including content variation identification unit, distribution analysis unit, and compression processing unit. Each module handles specific tasks independently, enabling parallel processing in hardware while maintaining modularity that reduces overall system complexity and cost.
Solution Approach 2:
The system dynamically adjusts compression parameters and processing depth based on input data characteristics. The compression ratio and processing cycles are adaptively controlled according to the identified content variations and distributions, optimizing speed-cost tradeoff for different data types without requiring fixed high-performance hardware for all cases.
2Loss of information
If complex structures are created and processed using multiple compression schemes, then compression gain is improved, but device complexity increases
Solution Approach 1:
The compression process is segmented into four distinct compression schemes, each targeting specific patterns of content variations and distributions. This segmentation allows the system to apply appropriate compression techniques selectively rather than using a single complex algorithm for all data types, reducing overall processing complexity while maintaining high compression gains.
Solution Approach 2:
The system applies compression processing in multiple passes or cycles, where each pass focuses on specific aspects of the data. Not all compression schemes are applied to all data uniformly - instead, the system selectively applies only the necessary processing depth and scheme combinations based on the identified content characteristics, avoiding unnecessary complexity.
3Loss of information
If multiple compression cycles are applied to achieve better compression, then compression gain is improved, but processing time increases
Solution Approach 1:
Before applying multiple compression cycles, the system performs preliminary analysis to identify content variations and distributions in the input data. This preliminary characterization allows the system to determine the optimal number of compression cycles needed and select appropriate schemes in advance, avoiding unnecessary processing cycles and reducing overall processing time while maintaining compression effectiveness.
Solution Approach 2:
The compression process uses periodic application of compression schemes in cycles, where each cycle processes specific aspects of the data. The system alternates between different compression schemes across multiple cycles rather than applying one scheme repeatedly, which improves compression gain while distributing processing load to reduce total processing time.
Data Source
AI summary
A binary data compression/decompression method is disclosed, where any input binary data string (IFDS) is uniquely and reversibly compressed/decompressed without any data loss by first uniquely formatting and fully describing the IFDS using a set of binary constructs, followed by creating complex structures from custom combinations of the binary constructs that occur within the IFDS content, wherein the choice of the custom combinations depend on the IFDS content therefore creating IFDS content variations and distributions from an expected nominal base reflecting the actual content of the IFDS, followed by uniquely processing these variations and distributions using several schemes, each bringing a unique compression feature, and wherein once this processing completes, another repeating compression cycle can be applied until the desired compressed file size or a file floor size limit is reached, size below which the disclosed compression has limitations.