Lossless compression catalyst based on binary allocation via modular arithmetic

TL;DR

This paper introduces BAMA, a modular arithmetic-based binary catalyst that enhances the performance of lossless compression algorithms for digital data without compressing directly.

Contribution

The paper presents BAMA, a novel modular arithmetic-based catalyst that improves the efficiency of existing lossless compression algorithms for binary data.

Findings

BAMA significantly increases compression performance.

Applicable to various digital data types.

Compatible with multiple lossless algorithms.

Abstract

A new binary (bit-level) lossless compression catalyst method based on a modular arithmetic, called Binary Allocation via Modular Arithmetic (BAMA), has been introduced in this paper. In other words, BAMA is for storage and transmission of binary sequences, digital signal, images and video, also streaming and all kinds of digital transmission. As we said, our method does not compress, but facilitates the action of the real compressor, in our case, any lossless compression algorithm (Run Length Encoding, Lempel-Ziv-Welch, Huffman, Arithmetic, etc), that is, it acts as a compression catalyst. Finally, this catalyst allows a significant increase in the compression performance of binary sequences, among others.