Analysis of Huffman Tree-Based Lossless Compression on Different Text Types

Lanzhi Luo; Qinyan Yu; Heng Zhang; Shengqi Pan

doi:10.6919/ICJE.202512_11(12).0017

Authors

Lanzhi Luo
Qinyan Yu
Heng Zhang
Shengqi Pan

DOI:

https://doi.org/10.6919/ICJE.202512_11(12).0017

Keywords:

Huffman Tree; Lossless Compression; MATLAB Simulation.

Abstract

This paper comprehensively investigates Huffman Tree-based lossless data compression across various text types, including technical reports, news articles, and fiction narratives. A detailed theoretical model for calculating compression ratios is first introduced and subsequently validated through systematic MATLAB simulations. The results clearly indicate that character frequency distribution significantly affects compression efficiency. Specifically, fiction narrative, with its higher redundancy and repetitive linguistic patterns, achieves the best performance, yielding the highest compression ratio. Conversely, more diverse and concise texts like technical documents show less dramatic gains. These findings robustly demonstrate Huffman coding's particular effectiveness and efficiency when applied to high-redundancy datasets, reinforcing its foundational role in classical compression theory.

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