Binary Code Compression Using Packetized Lookup Tables
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Solution Overview
Problem
Current data compression systems require knowledge of the data content to identify and remove redundancy, making them complex and inefficient, as they operate on the application layer rather than the physical layer, and are specific to different data types such as images, audio, and video.
Innovation Solution
A method and system that compresses binary code irrespective of data type using a simple, homogenous algorithm, packaging data into two-bit packets and using pre-generated compression key tables for pattern recognition, allowing for efficient compression and decompression of pure binary code without analyzing the data content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current compression systems analyze data content to remove redundancy, then compression effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the 8-bit binary data into multiple groups (e.g., four 2-bit groups) and applies independent random substitutions to each group. This segmentation allows the system to achieve compression without needing to analyze the overall data content, reducing complexity while maintaining effectiveness.
Solution Approach 2:
The patent introduces pre-generated lookup tables as an intermediary between the input data and compression output. These tables contain pre-computed substitution patterns that can be applied directly to data groups without real-time analysis, eliminating the need for complex redundancy detection algorithms.
2Quantity of substance
If data is compressed by removing redundant information, then file size is reduced, but loss of information occurs
Solution Approach 1:
The patent changes the parameter representation of data by substituting 2-bit groups with different 2-bit values from lookup tables. This parameter transformation achieves compression while maintaining the ability to reconstruct the original data exactly, preventing information loss.
Solution Approach 2:
The patent creates compressed representations by copying and substituting data groups according to pre-generated patterns. The lookup tables store multiple copies of substitution patterns that can be applied reversibly, ensuring the original information can be fully recovered during decompression.
3Productivity
If compression algorithms are tailored to specific data types, then compression ratio is improved, but adaptability decreases
Solution Approach 1:
The patent creates a universal compression system that works on any 8-bit binary data regardless of its semantic meaning or data type. The method applies the same grouping and substitution process to images, audio, video, or any other binary data, achieving versatility without sacrificing compression effectiveness.
Solution Approach 2:
The patent treats all input data as homogeneous 8-bit binary streams, ignoring any semantic differences between data types. By applying uniform grouping into 2-bit segments and consistent lookup table substitutions, the system achieves data-type independence while maintaining compression performance.
4Quantity of substance
If lossy compression is used to achieve higher compression ratios, then bandwidth usage is reduced, but quality deteriorates
Solution Approach 1:
The patent uses disposable-looking random substitution patterns from lookup tables that appear to lose information but actually preserve it. The substitution process uses simple, computationally inexpensive operations that achieve compression ratios similar to lossy methods while maintaining exact reconstructability like lossless methods.
Data Source
AI summary
A system, method, and apparatus for compressing binary code comprising at least a processor, memory, storage, and an encoding device or decoding device or both. The methods include comparing a given binary code string having a certain size to be compressed or decompressed with a table comprising all possible combinations of zeroes and ones for any binary data of size x. The given binary code string and all possible combinations are partitioned in packets of size y and each packet assigned a value. A second value representing an assembly of all the values into a second value is performed. The assembly may be performed n times to obtain an nth value. The second or nth value is transmitted or received or both by the encoding and decoding devices, respectively, in place of the given binary code. Table comparison may be performed using pattern recognition.