An Enhanced Extended Huffman Coding Approach for Efficient Binary Data Compression

Finding efficient and affordable ways to store and transmit digital data is becoming an urgent challenge. This research presents a method for lossless compression called Enhanced Extended Huffman Coding (EEHC). As opposed to the regular Binary Huffman Coding (BHC), which works with each symbol (the two values, 0 and 1), EEHC uses an algorithm that creates variable-size block based on the statistical frequency of data to create the associated multi-branch (base-8) Huffman tree that captures higher order (beyond the single symbols) statistical correlation between the data and, therefore, reduces the amount of redundant data. Comparative performance analysis indicates that the EEHC algorithm achieves substantial compression efficiency, yielding reductions of 77.49% (21,620 bytes from an initial 96,051-byte PDF file) and 86.00% (320,871 bytes from an original 2,291,925-byte MP4 file). These results demonstrate that EEHC attains a superior compression ratio relative to the conventional Binary Huffman Coding approach when applied to diverse digital media formats of comparable size. Additionally, the EEHC preserves lossless reconstruction; that is, it’s able to rebuild the original data exactly through the invertible multi-branch tree structure used for decompression. The results demonstrate that EEHC provides a good level of compression performance while maintaining the stability of the processing performance, and, therefore, makes the approach appropriate for many applications with large amounts of data.

Enhanced Extended Huffman Coding (EEHC), Lossless Data Compression

1. Shakya, A. K., & Vidyarthi, A. (2024). Comprehensive study of compression and texture integration for digital imaging and communications in medicine data analysis. Technologies, 12(2), 17. doi: 10.3390/technologies12020017 [Google Scholar] [Crossref]

2. Z. Zhang, Y. Liu, and W. Wang, "Fitted Neural Lossless Image Compression," in 2025 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, 2025. doi: 10.1109/CVPR.2025.01234 [Google Scholar] [Crossref]

3. Tu, K., & Puchala, D. (2022). Variable-to-variable Huffman coding: Optimal and greedy approaches. Entropy, 24(10), 1447. https://doi.org/10.3390/e24101447 [Google Scholar] [Crossref]

4. Mahfi, M. S., Hasan, M. M., & Hossain, G. (2025). OBHS: An optimized block Huffman scheme for real-time audio compression. arXiv preprint. https://arxiv.org/abs/2511.14793 [Google Scholar] [Crossref]

5. Kruger, D., Kumar, Y., & Li, J. J. (2025, March). Parametric Matching for Improved Data Compression. In 2025 Data Compression Conference (DCC) (pp. 383-383). IEEE. doi: https://doi.org/10.1109/DCC62719.2025.00070 [Google Scholar] [Crossref]

6. Sun, H., Ding, Y., Yi, L., Ma, H., Wang, G., Liu, X., ... & Cai, W. (2025, August). Pmklc: Parallel multi-knowledge learning-based lossless compression for large-scale genomics database. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (pp. 2725-2734). https://doi.org/10.3390/technologies12020017 [Google Scholar] [Crossref]

7. Garg, G., & Kumar, R. (2024). Adaptive block size selection in a hybrid image compression algorithm employing the DCT and SVD. International Journal on Smart Sensing and Intelligent Systems, 17(1). doi: https://doi.org/10.2478/ijssis-2024-0005 [Google Scholar] [Crossref]

8. Addepalli, P. S., & Lakshmi, P. V. (2024, February). Effective Medical Data Compression for Minimal Cloud Storage Using Versatile Compression Techniques. In International Conference on Algorithms and Computational Theory for Engineering Applications (pp. 43-51). Cham: Springer Nature Switzerland. doi: https://doi.org/10.1007/978-3-031-72747-4_7 [Google Scholar] [Crossref]

9. Huffman, D. A. (1952). A method for the construction of minimum-redundancy codes. Proceedings of the IRE, 40(9), 1098–1101. https://doi.org/10.1109/JRPROC.1952.273898 [Google Scholar] [Crossref]

10. Bi, K., Ma, Z., Xu, Q., Zhou, Y., Ma, Y., Sun, Q., & Lu, Z. (2026). DPCM-DP-EN: a lossless dynamic compress and encrypted encode method for DNA storage of medical images with high storage density. Medical & Biological Engineering & Computing, 64(2), 619-632. https://doi.org/10.1007/s11517-025-03458-z [Google Scholar] [Crossref]

11. Al-Serag, Z., Kim, J., & Srivastava, P. K. (2025). A novel lossless encoding algorithm for data compression–genomics data as an exemplar. Frontiers in Bioinformatics, *4*, 1489704. https://doi.org/10.3389/fbinf.2024.1489704 [Google Scholar] [Crossref]

12. Hameed, M. E., Mohammed, S. D., Hussein, W. K., Gupta, S., Anitha, R., Sonwalkar, P. K., & Ibrahim, M. M. (2025). Real-time ECG compression with adaptive Huffman coding: Improving data transmission in e-healthcare. Journal of Information Systems Engineering and Management, 10(15), 78-92. https://doi.org/10.52783/jisem.v10i33s.5746 [Google Scholar] [Crossref]

13. Lyu, F., Xiong, Z., Li, F., Yue, Y., & Zhang, N. (2025). An effective lossless compression method for attitude data with implementation on FPGA. Scientific Reports, 15(1), 13809. https://doi.org/10.1038/s41598-025-98372-7 [Google Scholar] [Crossref]

14. Klein, S. T., Opalinsky, E., & Shapira, D. (2024). Synchronizing dynamic Huffman codes. Discrete Applied Mathematics, 358, 23-32. https://doi.org/10.1016/j.dam.2024.06.034 [Google Scholar] [Crossref]

15. Xiao, X., Li, K., & Zhao, C. (2023). A hybrid compression method for compound power quality disturbance signals in active distribution networks. Journal of Modern Power Systems and Clean Energy, 11(6), 1902-1911. https://doi.org/10.35833/MPCE.2022.000602 [Google Scholar] [Crossref]

16. Rezasoltani, S., & Qureshi, F. Z. (2024). Hyperspectral image compression using sampling and implicit neural representations. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-12. https://doi.org/10.1109/TGRS.2024.3509718 [Google Scholar] [Crossref]

17. Cheng, F., Guo, C., Wei, C., Zhang, J., Zhou, C., Hanson, E., ... & Chen, Y. (2025, June). Ecco: Improving memory bandwidth and capacity for llms via entropy-aware cache compression. In Proceedings of the 52nd Annual International Symposium on Computer Architecture (pp. 793-807). https://doi.org/10.1145/3695053.3731024 [Google Scholar] [Crossref]

18. Sharma, A., & Barde, S. (2025). Enhancing greedy variable-to-variable Huffman coding using entropy-based heuristics and adaptive m-gram analysis. Utilitas Mathematica. https://utilitasmathematica.com/index.php/Index/article/view/2375 [Google Scholar] [Crossref]

19. Ezra, B., Manga, I., & Sarjiyus, O. (2025). A Review on RSA-Huffman Hybrid: towards Secure, Compressed and Adaptive Network Data Transmission. Asian Journal of Research in Computer Science, 18(7), 60-76. https://doi.org/10.9734/ajrcos/2025/v18i7720 [Google Scholar] [Crossref]

20. Odat, A., Otair, M., & Al-Khalayleh, M, "Comparative study between LM-DH technique and Huffman coding.," International Journal of Applied Engineering Research, no. 10(15), pp. 36004-36011, 2015 [Google Scholar] [Crossref]

21. F. Lyu, Z. Xiong, F. Li, Y. Yue, and N. Zhang, "An effective lossless compression method for attitude data with implementation on FPGA," Scientific Reports, vol. 15, no. 1, p. 13809, 2025. doi: https://doi.org/10.1038/s41598-025-98372-7 [Google Scholar] [Crossref]

22. Pintilei, M. A., Schreiner, C., & Socotar, D, "October). Exploring Data Compression: Solutions to Optimize Efficiency and Improve Performance," In 2024 IEEE International Conference And Exposition On Electric And Power Engineering (EPEi) , pp. 280-286, 2024. [Google Scholar] [Crossref]

23. Mahfi, M. S., Hasan, M. M., & Hossain, G, "OBHS: An Optimized Block Huffman Scheme for Real-Time Audio Compression. arXiv preprint arXiv:.," p. 2511.14793, 2025. [Google Scholar] [Crossref]

24. Y. Wiseman, " High-Speed Architecture for Hybrid Arithmetic–Huffman Data Compression. Technologies,," no. 13(12), p. 585, 2025. [Google Scholar] [Crossref]

25. F. Marcelloni and M. Vecchio, “An efficient lossless compression algorithm for tiny nodes of monitoring wireless sensor networks,” The Computer Journal, vol. 52, no. 8, pp. 969–987, 2009. doi: https://doi.org/10.1093/comjnl/bxp035 [Google Scholar] [Crossref]

26. Z. Wang, L. Huang, and S. Wang, “A survey on data compression in wireless sensor networks,” Journal of Network and Computer Applications, vol. 143, pp. 102–115, 2019. doi: https://doi.org/10.1016/j.jnca.2019.06.006 [Google Scholar] [Crossref]

27. A. Kiely and M. Klimesh, “The ICER progressive wavelet image compressor,” Interplanetary Network Progress Report, vol. 42, no. 155, pp. 1–46, 2005. doi: https://doi.org/10.1002/0471730590 [Google Scholar] [Crossref]

28. D. Podgorelec, D. Strnad, I. Kolingerová, and B. Žalik, “State-of-the-art trends in data compression: COMPROMISE case study,” Entropy, vol. 26, no. 12, p. 1032, 2024. doi: https://doi.org/10.3390/e26121032 [Google Scholar] [Crossref]

29. K. L. Ketshabetswe, A. M. Zungeru, B. Mtengi, C. K. Lebekwe, and S. R. S. Prabaharan, “Data compression algorithms for wireless sensor networks: A review and comparison,” IEEE Access, vol. 9, pp. 136872–136891, 2021. doi: https://doi.org/10.1109/ACCESS.2021.3116311 [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet