Authors : Pulak Kumar Jena; Ramkumar Ghadai; Sudarshan Murmu; Binod Kumar Baliar Sing

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/fb3wy8pf

Scribd : https://tinyurl.com/mjashmam

DOI : https://doi.org/10.38124/ijisrt/26May1486

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid advancement of digital communication systems and multimedia technologies has significantly increased the amount of data generated, transmitted, and stored across computer networks. As a result, efficient data compression techniques have become essential for minimizing storage requirements and reducing transmission bandwidth while maintaining data integrity. Data compression is a process that eliminates redundancy from digital information to represent data using fewer bits than the original format. Among various lossless compression techniques, Huffman Coding remains one of the most effective and widely adopted methods due to its simplicity, optimality, and efficient implementation characteristics. Huffman Coding is a statistical compression technique that generates variable-length binary codes based on the probability of occurrence of symbols within a dataset. Frequently occurring symbols are assigned shorter binary codes, while less frequent symbols receive longer codes, thereby reducing the average code length of the encoded data. This approach achieves efficient compression without any loss of information, making it suitable for applications where exact reconstruction of original data is required. Huffman Coding has been extensively used in file compression systems, text processing applications, image compression standards, and digital communication systems [1]. This research paper presents the implementation and analysis of Huffman Coding and Decoding using MATLAB. MATLAB provides a powerful computational environment for simulating digital communication algorithms and analyzing compression performance. The proposed work focuses on constructing Huffman trees based on symbol probabilities, generating optimal prefix codes, encoding input data into compressed binary sequences, and reconstructing the original information through the decoding process. The MATLAB implementation demonstrates the practical realization of Huffman Coding and evaluates the effectiveness of the algorithm using different performance parameters such as compression ratio, average code length, coding efficiency, and redundancy. The research further analyzes how symbol probability distribution affects the overall compression performance. Experimental results indicate that Huffman Coding achieves higher compression efficiency when symbol frequencies are non-uniform. The encoded output generated through MATLAB simulation significantly reduces data size compared to fixedlength coding schemes while ensuring accurate recovery of the original message during decoding. The lossless nature of the algorithm makes it highly reliable for applications involving sensitive textual, image, and multimedia data [2]. In addition to implementation and performance evaluation, this paper discusses the advantages, limitations, and practical applications of Huffman Coding in modern communication systems. Although several advanced compression methods such as Arithmetic Coding and Lempel-Ziv-Welch (LZW) have been developed, Huffman Coding continues to remain a fundamental technique because of its lower computational complexity and efficient real-time performance. The study also highlights possible future improvements including adaptive Huffman Coding and hybrid compression methods integrated with intelligent algorithms [3]. The overall objective of this work is to provide a detailed understanding of lossless data compression using Huffman Coding and to demonstrate its practical implementation in MATLAB for educational and research purposes. The proposed system proves that Huffman Coding remains an effective solution for minimizing data storage requirements and improving communication efficiency in digital systems.

Keywords : Data Compression, Huffman Coding, MATLAB, Lossless Compression, Encoding, Decoding, Compression Ratio, Information Theory.

References :

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