International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

The Evolution of Data Compression Algorithms: From Huffman Coding to Modern AI-Based Compression Techniques

Author(s) Mr. Abhav Jain
Country India
Abstract Data compression is essential for reducing the storage and transmission requirements of the rapidly growing volume of digital data. This study examines the evolution of data compression techniques from classical entropy coding methods to modern approaches that incorporate security and machine learning. It reviews lossless and lossy compression and analyzes classical methods including Huffman and arithmetic coding, along with adaptive compression techniques. The study further explores chaos-based compression methods that integrate data reduction with security through chaotic sequences, dynamic S-Boxes, and secure adaptive coding. Neural compression is also examined, focusing on neural networks for context modeling, deep model compression, end-to-end learned image compression, and implicit neural representations. Through secondary analysis of existing literature, the study compares these approaches based on compression efficiency, processing time, computational complexity, security, adaptability, and practical applicability. The findings indicate that different methods involve trade-offs, suggesting that hybrid approaches combining classical compression, security mechanisms, and machine learning may offer useful directions for future research.
Keywords Data Compression, Huffman Coding, Arithmetic Coding, Adaptive Compression, Chaos-Based Compression, Neural Compression, Machine Learning, Lossless Compression, Lossy Compression, Data Security, Compression Efficiency, Hybrid Compression.
Published In Volume 8, Issue 5, September-October 2026
Published On 2026-10-05
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals