Cellular Automata Pre-Processing for Random-Like File Compression
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Solution Overview
Problem
Existing file compression methods are inefficient when dealing with random strings of characters, such as SSH keys, as they require an exact match between the original and compressed files, leading to high computing power usage and poor compression ratios.
Innovation Solution
The method involves creating an intermediary file using cellular automata pre-processing, which transforms the original file into a more compressible format through an optimal binary mask, allowing for efficient compression and decompression without requiring an exact match between the original and final states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If standard compression algorithms are used on random strings of characters (e.g., SSH keys), then the compression process is simple and fast, but the compression ratio is poor and computing power is wasted
Solution Approach 1:
The patent applies preliminary action by performing cellular automata pre-processing on the original file before compression. This transforms the file into an intermediary format that is more amenable to compression, creating patterns that standard algorithms can efficiently exploit. The pre-processing step prepares the data in advance to enable better compression ratios without sacrificing speed.
Solution Approach 2:
The patent introduces an intermediary file as a mediator between the original file and the compressed output. This intermediary file is generated through cellular automata transformation and serves as a bridge that standard compression algorithms can process efficiently. The intermediary format contains structured patterns that are much more compressible than the original random data.
2Area of moving object
If cellular automata methods are used to search for an optimal final state (Haukeli method), then compression efficiency may improve, but computing power usage increases significantly due to searching many possible solutions
Solution Approach 1:
Instead of searching for an optimal final state after compression, the patent performs preliminary cellular automata transformation before compression. This deterministic pre-processing creates an intermediary file with inherent compressibility, eliminating the need for exhaustive searching of possible solutions and significantly reducing computing power requirements.
Solution Approach 2:
The patent inverts the traditional approach by applying cellular automata in reverse: rather than transforming the compressed output back to find an optimal original state (Haukeli method), it transforms the original input into an intermediary state that is pre-optimized for compression. This inversion eliminates the need for backward searching and reduces computational complexity.
3Productivity
If cellular automata pre-processing is applied to create an intermediary file, then compression efficiency and ratio improve, but the overall process complexity increases
Solution Approach 1:
The patent segments the compression process into two distinct stages: cellular automata pre-processing to create an intermediary file, and standard compression algorithm application. This segmentation allows each stage to be optimized independently - the pre-processing creates patterns while the standard algorithm handles the actual compression, managing overall complexity through modular design.
Solution Approach 2:
The patent achieves universality by making the cellular automata pre-processing applicable to any file type that can be represented as binary data. The method works with SSH keys, images, audio, and other formats, providing a universal pre-processing step that enhances the effectiveness of standard compression algorithms across diverse data types without requiring format-specific complexity.
Data Source
AI summary
Described herein relates to a method for pre-processing files that can improve file compression rates of existing general-purpose lossless file compression algorithms, particularly for files on which traditional algorithms perform poorly. The elementary cellular automata (CA) pre-processing technique involves finding an optimal CA state that can be used to transform an original file into a format (i.e., an intermediary file) that is more amenable to compression than the original file format. This technique is applicable to multiple file types and may be used to enhance multiple compression algorithms. Evaluation on generated files, as well as samples selected from online text repositories, finds that the CA pre-processing technique improves compression rates by up to 4% and shows promising results for assisting in compressing data that typically induce worst-case behavior in standard compression algorithms.