Enhanced Lossless Data Compression Using Logistic Context Mixing and Predictive Analysis

Overview

Compression is useful because it reduces resources required to store and transmit data. It is used to represent data in relatively smaller number of bits. Many now-a-days used general purpose compression algorithms are mostly customised versions of Lempel-Ziv(LZ) algorithm families or consist of primitive-standard-algorithms such as DEFLATE. Compression algorithms are designed on the grounds of what purpose they serve, for example organisations like facebook stress of decompression-speed in their algorithms and not about ratio or Compression-speed. There are two categories of compression-techniques, lossy and lossless. Whilst each uses different techniques to compress files, both have the same aim:To look for duplicate data in a file and use a much more compact data representation. Lossless-compression reduces bits by identifying and eliminating statistical-redundancy. No information is lost in lossless-compression. This project focuses on making a universal-lossless-compression algorithm and on the concept of Context-Mixing(for text), but with a practical end-user compression. We have attempted to create algorithms that attain enormous compression-ratios but with a practical timespan, that is we try to increase compression-ratios while increasing compression/decompression-speed, by using derivations from Logistic-Context-mixing algorithms(uses Neural-Networks). These two are the main focuses of this project and can be proved useful in data archiving or secure and efficient transmission of data. Context-mixing is a type of data compression algorithm in which the next-symbol predictions of two or more statistical models are combined to yield a prediction that is often more accurate than any of the individual predictions.

Competition history

• ISEF 2020 Systems Software · Entry SOFT033

Resources

• ISEF abstract page ↗

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