A Lossless Compression Method for Chat Messages Based on Huffman Coding and Dynamic Programming

Julián Moreno Cadavid,Hernán Darío Vanegas Madrigal

doi:10.3390/computers10030028

Julián Moreno Cadavid, Hernán Darío Vanegas Madrigal

Open Access

https://doi.org/10.3390/computers10030028

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Abstract

There is always an increasing demand for data storage and transfer; therefore, data compression will always be a fundamental need. In this article, we propose a lossless data compression method focused on a particular kind of data, namely, chat messages, which are typically non-formal, short-length strings. This method can be considered a hybrid because it combines two different algorithmic approaches: greedy algorithms, specifically Huffman coding, on the one hand and dynamic programming on the other (HCDP = Huffman Coding + Dynamic Programming). The experimental results demonstrated that our method provided lower compression ratios when compared with six reference algorithms, with reductions between 23.7% and 39.7%, whilst the average remained below the average value reported in several related works found in the literature. Such performance carries a sacrifice in speed, however, which does not presume major practical implications in the context of short-length strings.

Highlights

There is always an increasing demand for data storage and transfer; data compression will always be a fundamental need
Considering the above, in this paper, we present a lossless compression method based on a traditional algorithm: Huffman coding
Lossless compression is a crucial matter in computing, when resources, like bandwidth or storage, are expensive

Summary

Introduction

There is always an increasing demand for data storage and transfer; data compression will always be a fundamental need. We propose a lossless data compression method focused on a particular kind of data, namely, chat messages, which are typically non-formal, short-length strings. The experimental results demonstrated that our method provided lower compression ratios when compared with six reference algorithms, with reductions between 23.7% and 39.7%, whilst the average remained below the average value reported in several related works found in the literature. Such performance carries a sacrifice in speed, which does not presume major practical implications in the context of short-length strings

Methods

Results

Conclusion