Binary Data Compression Pipeline for Fast Lossless Hardware Encoding
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Solution Overview
Problem
Existing binary compression/decompression methods lack efficiency and speed, particularly in hardware implementations, which are essential for certain applications requiring high performance.
Innovation Solution
The proposed binary compression/decompression method employs a fully-pipelined architecture with three compression schemes (Mathematical Processing String, Mathematical Root Identifier pairings, and extensions) that allow for multiple compression cycles, achieving lossless restoration and high speed, particularly in hardware implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a hardware implementation is used, then compression/decompression speed is improved, but manufacturing cost increases
Solution Approach 1:
The compression and decompression operations are segmented into distinct pipeline stages including partitioning, encoding, decoding, and assembly. Each stage processes data independently and concurrently, enabling high-speed parallel operation in hardware while keeping each individual stage relatively simple and cost-effective to implement.
Solution Approach 2:
Data is pre-processed through partitioning into fixed-size blocks before compression begins. This preliminary organization of data into standardized units enables the subsequent compression stages to operate efficiently and predictably, improving overall speed while using simple, repeatable hardware structures.
2Productivity
If multiple compression schemes are implemented, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The compression process is divided into three distinct schemes (MPS, MRI, and MPS for RI2) that operate on different types of data patterns. Each scheme handles specific compression scenarios, allowing the system to achieve high overall efficiency while keeping each individual scheme relatively simple and manageable.
Solution Approach 2:
The system dynamically selects which compression scheme to apply based on the characteristics of the input data. This dynamic adaptation allows the compressor to achieve high efficiency across diverse data types without requiring a single overly complex algorithm, maintaining device simplicity while improving productivity.
3Loss of substance
If repeated compression cycles are applied, then compression ratio is improved, but processing time increases
Solution Approach 1:
Multiple compression cycles are executed in continuous succession without interrupting the data flow. The output of one cycle immediately becomes the input for the next cycle, maintaining continuous processing. This approach achieves high compression ratios while minimizing idle time and keeping the overall processing time acceptable.
Solution Approach 2:
The data is prepared and organized before entering the repeated compression cycles, with partitioning and initial processing completed in advance. This preliminary action enables the subsequent multiple compression cycles to proceed efficiently without additional setup time, improving compression ratio while controlling 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 transforming the IFDS in well defined variable length binary constructs using well defined criteria and procedure, followed by partitioning the IFDS in IFDS slices of optimal sizes based on well defined criteria, where each IFDS slice is individually compressed according to three independent schemes and several extensions that are all based on mathematical description of well defined relationships noted within the content of such an IFDS slice in term of specific binary constructs, leading to unique identifiable binary entities that are used to generate compression gain by reassignment of specific well defined parts of the data constructs within the subject IFDS slice, where such compressed IFDS slices are assembled in the same original slice order as found in the original uncompressed IFDS, creating the full compressed IFDS, where an unlimited but defined number of such compression cycles are executed until the desired final file size is achieved.