A Universal 4D Model for Double-Efficient Lossless Data Compressions

TL;DR

This paper introduces a universal 4D model for lossless data compression using a fuzzy-binary and-or algorithm that achieves significant compression ratios and demonstrates potential for scalable database and network applications.

Contribution

The paper presents a novel 4D hypercube-based compression model combining fuzzy-binary logic with a fixed translation table, enabling efficient lossless data compression and decompression.

Findings

Achieves approximately 50% compression ratios.

Demonstrates negative entropy growth for >87.5% compression.

Offers a universal predictability model for scalable data systems.

Abstract

This article discusses the theory, model, implementation and performance of a combinatorial fuzzy-binary and-or (FBAR) algorithm for lossless data compression (LDC) and decompression (LDD) on 8-bit characters. A combinatorial pairwise flags is utilized as new zero/nonzero, impure/pure bit-pair operators, where their combination forms a 4D hypercube to compress a sequence of bytes. The compressed sequence is stored in a grid file of constant size. Decompression is by using a fixed size translation table (TT) to access the grid file during I/O data conversions. Compared to other LDC algorithms, double-efficient (DE) entropies denoting 50% compressions with reasonable bitrates were observed. Double-extending the usage of the TT component in code, exhibits a Universal Predictability via its negative growth of entropy for LDCs > 87.5% compression, quite significant for scaling databases and network communications. This algorithm is novel in encryption, binary, fuzzy and information-theoretic methods such as probability. Therefore, information theorists, computer scientists and engineers may find the algorithm useful for its logic and applications.