Implementing Huffman Coding for Data Compression

Abstract

This paper provides a thorough comparison between the Quaternary Tree Structure and M-Gram Entropy Variable to Variable Coding variations of the traditional Huffman data compression algorithm. The main goal is to analyze the original Huffman algorithm\'s binary tree code structure and compare it with the quaternary tree structure used in quaternary tree compression. Furthermore, the paper explores the theoretical foundation and application of the novel M-Gram Entropy Variable to Variable Coding method. By closely examining encoding processes, decoding mechanisms, and compression effectiveness, this work seeks to clarify the unique features and comparative advantages of each technique.This work aims to offeruseful insights fordata compression researchers and practitioners by illuminating the trade-offs between compression ratio, computational complexity, and adaptation to various data kinds.

Introduction

This research focuses on Huffman coding, a widely used lossless data compression technique that uses binary trees to assign variable-length codes to characters based on their frequency, resulting in efficient compression of text files. Huffman coding reduces file sizes by replacing frequently occurring characters with shorter codes, speeding up data transmission and saving storage without data loss. The study compares the binary tree approach with more complex alternatives like quaternary trees, concluding that binary trees offer the best balance of simplicity, speed, and compression efficiency for text data.

The implementation involves building a Huffman tree from character frequencies, generating prefix-free codes, and encoding the input data accordingly. The paper highlights Huffman coding’s advantages over earlier lossy compression models, especially for text data where lossless compression is essential. Experimental results demonstrate significant file size reductions and fast compression/decompression with preserved data integrity. Overall, the binary tree-based Huffman coding method is effective, versatile, and suitable for various applications requiring lossless text compression.

Conclusion

Tosumup,thisresearchonHuffmancodingdatacompressionhasshedlightonthe effectivenessandadaptabilityofthisessentialcompressionmethod.Wehaveshown through the implementation that Huffman coding may drastically reduce file sizes while maintainingdataintegrity,makingitapracticaloptionforarangeofapplicationsthat need effective data transmissionand storage. This investigationinto Huffmancodinghasdemonstrateditsflexibilityandeffectiveness, especially when applied with a binary tree technique.

References

[1] Malik, N. Goyat and V. Saroha, \"Greedy Algorithm: Huffman Algorithm,\" International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 296-303, 2013. [2] A. S. Sidhu and M. Garg, \"Research Paper on Text Data Compression Algorithm using Hybrid Approach,\" IJCSMC, vol. 3, no. 12, pp. 1-10, 2014. [3] H. Al-Bahadili and S. M. Hussain, \"A Bit-level Text Compression Scheme Based on the ACW Algorithm,\" International Journal of Automation and Computing, pp. 123- 131, 2010. [4] I. Akman, H. Bayindir, S. Ozleme, Z. Akin and a. S. Misra, \"Lossless Text Compression Technique Using Syllable Based Morphology,\" International Arab Journal of Information Technology, vol. 8, no. 1, pp. 66-74, 2011. [5] M. Schindler, \"Practical Huffman coding,\" 1998. [Online]. Available: http://www.compressconsult.com/huffman/. [6] R.S. Brar and B. Singh, “A survey on different compression techniques and bit reduction Algorithm for compression of text data” International Journal of Advanced Research In Computer Science and Software Engineering (IJARCSSE) Volume 3, Issue 3, March 2013 [7] S. Porwal, Y. Chaudhary, J. Joshi, and M. Jain, “Data Compression Methodologies for Lossless Data and Comparison between Algorithms” International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 2, March 2013 [8] S. Shanmugasundaram and R. Lourdusamy, “A Comparative Paper of Text Compression Algorithms” International Journal of Wisdom Based Computing, Vol.1 (3), Dec 2011 [9] S. Kapoor and A. Chopra, \"A Review of Lempel Ziv Compression Techniques\" IJCST Vol.4, [10] Issue 2, April-June 2013 [11] S.R. Kodituwakku and U. S. Amarasinghe, “Comparison of Lossless Data Compression Algorithms for Text Data “Indian Journal of Computer Science & Engineering Vol 1 No 4 [12] R. Kaur and M. Goyal, “An Algorithm for Lossless Text Data Compression” International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 7, July - 2013 [13] H. Altarawneh and M. Altarawneh, \"Data Compression Techniques on Text Files: A Comparison Paper” International Journal of Computer Applications, Vol 26– No.5, and July 2011 [14] U. Khurana and A. Koul, “Text Compression and Superfast Searching” Thapar Institute of Engineering and Technology, Patiala, Punjab, India-147002 [15] Tito WaluyoPurboyo and AnggunmekaLuhurPrasasti, \"A review of data compressiontechniques,\"International Journal of Applied Engineering Research, vol. 12, no. 19, pp. 8956-8963, Jan. 2017. [16] A. Habib, M. J. Islam, and M. S. Rahman, \"Quaternary Tree Structure as a Novel Method for Huffman Coding Tree,\" J. Comput. Sci., vol. 19, no. 9, pp. 1132-1142, 2023. Available: https://doi.org/10.3844/jcssp.2023.1132.1142.

Copyright

Copyright © 2025 Sandeep Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.