Binary Data Compression Using RI-DL Processing Strings
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Solution Overview
Problem
Existing binary data compression/decompression methods lack efficiency in achieving lossless compression and decompression, particularly in hardware implementations, where speed and cost trade-offs hinder optimal performance across various applications.
Innovation Solution
The Binary Data Compression/Decompression Method (BDCD) serially formats Initial Full Data Strings into Processing Strings using Root Identifiers (RI) and Detail parts, creating complex structures to achieve compression through various schemes, allowing for multiple cycles of compression and decompression, mirroring the process for lossless restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware implementation is used to achieve high compression speed, then compression/decompression speed is improved, but implementation cost increases
Solution Approach 1:
The patent segments the binary data into fixed-length Processing Strings (PS) of 32 bits each, with each PS containing a Root Identifier (RI) and Detail (DL) parts. This segmentation enables parallel processing of multiple data segments in hardware, achieving high compression speed while keeping individual processing units simple and cost-effective.
Solution Approach 2:
The patent transforms the compression problem from sequential bit-by-bit processing to parallel Processing String level processing. By organizing data into fixed-length 32-bit blocks with structured RI-DL components, the system enables simultaneous processing of multiple data elements in hardware, dramatically improving throughput without proportionally increasing complexity.
2Loss of substance
If complex compression structures are used to achieve high compression ratio, then compression efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent applies different processing rules to different parts of the Processing String: the Root Identifier (RI) part uses one set of compression rules while the Detail (DL) part uses another. This local differentiation allows optimized compression strategies for each segment, achieving high compression ratios without requiring complex unified processing logic across the entire data structure.
Solution Approach 2:
The patent changes the representation parameters of binary data by encoding it into Processing Strings with specific RI-DL structures. By transforming the data into this structured format with defined bit allocations and encoding rules, the system achieves efficient compression through parameter-based transformations rather than complex algorithmic processing.
3Ease of manufacture
If fixed Processing String length is used to simplify hardware implementation, then implementation simplicity is improved, but data representation flexibility decreases
Solution Approach 1:
The patent creates a universal Processing String format that can represent diverse binary data types through the RI-DL structure. The fixed 32-bit PS length provides hardware implementation simplicity, while the flexible RI (Root Identifier) and DL (Detail) components accommodate various data patterns and compression scenarios, making the system adaptable to different applications including audio broadcasting and telephony.
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 well defined binary constructs, followed by creating complex structures from custom combinations of said binary constructs that occur within the arbitrary IFDS content, wherein the choice of the said custom combinations depend on the said IFDS content in term of binary constructs therefore creating IFDS content variations and distributions from an expected nominal base wherein said variations and distributions reflect the actual content of the arbitrary IFDS, followed by uniquely processing these variations and distributions in content using several schemes where each scheme brings a unique compression feature, and wherein once this processing completes (i.e. the end of the arbitrary IFDS is reached), it is called that the end of one compression cycle is reached, and wherein another compression cycle can be applied to the data by repeating the cycle steps, and where such compression cycles are repeated until the desired compressed file is reached or until a file floor size limit is reached, floor size below which the disclosed compression has limitations.