Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 5 | Year 2026 | Article Id. IJECE-V13I5P121 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I5P121

A Novel Dual-Stage Compression Model Using RGC Bit-Planes and Refined Huffman Coding

T. Kavitha, S.V.R. Manimala, J. Pandu

Received Revised Accepted Published
17 Feb 2026 17 Mar 2026 20 Apr 2026 27 May 2026

Citation :

T. Kavitha, S.V.R. Manimala, J. Pandu, "A Novel Dual-Stage Compression Model Using RGC Bit-Planes and Refined Huffman Coding," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 258-271, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P121

Abstract

The need for cost-effective image compression keeps increasing even after the availability of numerous advanced methods. Though diverse means have been put forward, the problem of drastically effective compression still stands. This work presents an influential lossless and lossy compression method for grayscale images with the help of the Reflected Grey Code (RGC) that is used together with a newly designed Refined Huffman (RH) coding scheme, dubbed as RGC+RH. The 8-bit pixel intensities are initially converted into RGC, followed by Bit-Plane Slicing (BPS), and then RH coding is performed on each bit plane. In the lossless mode, all eight coded planes are transmitted and employed for the reconstruction. In the lossy mode, only the chosen Most Significant Bit (MSB) planes are taken into account. The efficiency of the suggested method is tested against that of the standard Modified Huffman (MH) technique by using the same metrics: Compression Ratio (CR), Percentage Memory Saving (PMS), Bits Per Pixel (BPP), Peak Signal-to-Noise Ratio (PSNR), and Mean Square Error (MSE).

Keywords

Bits Per Pixel (BPP), Bit-Plane Slicing (BPS), Peak Signal-to-Noise Ratio (PSNR), Refined Huffman (RH), Reflected Grey Code (RGC).

References

  1. Khalid Sayood, Introduction to Data Compression, 3rd ed., Morgan Kaufmann Publishers, pp. 1-680, 2005.
    [Google Scholar] [Publisher Link]
  2. David A. Huffman, “A Method for the Construction of Minimum-Redundancy Codes,” Proceedings of the IRE, vol. 40, no. 9, pp. 1098-1101, 1952.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. V.B. Balakirsky, “Joint Source-channel Coding with Variable Length Codes,” Proceedings of the IEEE International Symposium on Information Theory, Ulm, Germany, 1997.
    [CrossRef] [Google Scholar] [Publisher Link]
  4. Sai Han, and Tim Fingscheidt, “Variable-length Versus Fixed-length Coding: On Tradeoffs for Soft-decision Decoding,” 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, pp. 4269-4273, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. Huang-Chih Kuo, and Youn-Long Lin, “A Hybrid Algorithm for Effective Lossless Compression of Video Display Frames,” IEEE Transactions on Multimedia, vol. 14, no. 3, pp. 500-509, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Kun Tu, and Dariusz Puchala, “Variable-to-Variable Huffman Coding: Optimal and Greedy Approaches,” Entropy, vol. 24, no. 10, pp. 1-20, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  7. K. Padmanaban et al., “Data Communication through Reliable Sensor using Huffman Coding System,” Journal of Physics: Conference Series: Advances in Computer Science Engineering, vol. 1964, pp. 1-5, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Kuo-Nan Chen et al., “Embedding Secret Messages Using Modified Huffman Coding,” 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, Japan, pp. 278-281, 2009.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Chandrajit Pal et al., “Modified Huffman based Compression Methodology for Deep Neural Network Implementation on Resource Constrained Mobile Platforms,” 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy,  pp. 1-5, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. R. W. Doran, “The Gray Code,” University of Auckland, CDMTCS Research Report Series, pp. 1-25, 2007.
    [Google Scholar] [Publisher Link]
  11. E. S. Deutsch, “On the use of Binary and Gray Code Schemes for Continuous-tone Picture Representation,” Pattern Recognition, vol. 5, no. 2, pp. 121-132, 1973.
    [CrossRef] [Google Scholar] [Publisher Link]
  12. Darko Dimitrov et al., “Gray Code Compression,” Conference proceedings 20th International Workshop Combinatorial Algorithms, Hradec nad Moravici, pp. 183-193, 2009.
    [CrossRef] [Google Scholar] [Publisher Link]
  13. Kang Hyeon Rhee, “Forensic Detection Using Bit-Planes Slicing of Median Filtering Image,” IEEE Access, vol. 7, pp. 92586-92597, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  14. Hassan K. Albahadily, V. Yu. Tsviatkou, and V.K. Kanapelka, “Grayscale Image Compression using Bit Plane Slicing and Developed RLE Algorithms,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 6, no. 2, pp. 309-314, 2017.
    [Google Scholar] [Publisher Link]
  15. Heru Cahya Rustamaji, Mariani Mariani, and Bambang Yuwono, “Data Compression Application using Static Huffman Method On Android-Based Mobile Devices,” Telematika: Journal of Informatics and Information Technology, vol. 11, no. 1, pp. 9-18, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Simon M. Law, “Method of Building an Adaptive Huffman Code Word Tree,” US Patent 6040790, pp. 1-6, 2000.
    [Google Scholar] [Publisher Link]
  17. Adina Arthur, and V. Saravanan, “Efficient Medical Image Compression Technique for Telemedicine Considering Online and Offline Application,” 2012 International Conference on Computing, Communication and Applications, Dindigul, India, pp. 1-5, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. David A. Huffman, “A Method for the Construction of Minimum Redundancy Codes,” Resonance, vol. 11, pp. 91-99, 2006.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. P. Xiao, “Image Compression by Wavelet Transform,” Master’s Thesis, East Tennessee State University, pp. 1-70, 2001.
    [Google Scholar] [Publisher Link]
  20. V. Dai, and A. Zakhor, “Lossless Compression of VLSI Layout Image Data,” IEEE Transactions on Image Processing, vol. 15, no. 9, pp. 2522-2530, 2006.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Vito Dai, and Avideh Zakhor, “Advanced Low-Complexity Compression for Maskless Lithography Data,” Proceedings Emerging Lithographic Technologies, Santa Clara, California, United State, vol. 5374, 2004.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Rajiv Ranjan, and Prabhat Kumar, “An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding,” Entropy, vol. 25, no. 10, pp. 1-24, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. P. Sreenivasulu, and S. Varadarajan, “An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm,” Journal of Intelligent Systems, vol. 29, no. 1, pp. 1063-1078, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Ujjaval Yogesh Desai, “Coding of Segmented Image Sequences,” Master’s Thesis, Massachusetts Institute of Technology, pp. 1-74, 1994.
    [Google Scholar] [Publisher Link]
  25. Francesc Aulí-Llinàs, Joan Bartrina-Rapesta, and Miguel Hernández-Cabronero, “Probability Models for Highly Parallel Image Coding Architecture,” Signal Processing: Image Communication, vol. 112, pp. 1-8, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. Ferda Ernawan, Nomani Kabir, and Kamal Zuhairi Zamli, “An Efficient Image Compression Technique using Tchebichef Bit Allocation,” Optik, vol. 148, pp. 106-119, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. Seong-Won Lee, and Ho-Young Kim, “An Energy-efficient Low-memory Image Compression System for Multimedia IoT Products,” EURASIP Journal on Image and Video Processing, vol. 2018, pp. 1-15, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Matthew Friederich, and James A. Meek, “Method and System for Compressing Data and a Geographic Database Formed Therewith and Methods for use Thereof in a Navigation Application Program,” US Patent 6393149, 2002.
    [Publisher Link]
  29. Yew-San Lee et al., “Construction of Error Resilient Synchronization Codeword for Variable-length Code in Image Transmission,” Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), Vancouver, Canada, vol. 3, pp. 360-363, 2000.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. Hadeel Saleh Haj Aliwi et al., “Signaling Message Time for Homogeneous and Heterogeneous Protocols: A Comparative Study,” International Journal of Advanced and Applied Sciences, vol. 3, no. 8, pp. 14-17, 2016.
    [Google Scholar] [Publisher Link]
  31. Shashi Kumar M Kolavi, “Method and System for Value-based Data Compression,” US Patent 7079053, pp. 1-19, 2006.
    [Google Scholar] [Publisher Link]
  32. Jinwen Tian et al., “A Novel Image Compression Encoding,” Proceedings of Third International Conference on Signal Processing (ICSP'96), Beijing, China, vol. 2, pp. 982-985, 1996.
    [CrossRef] [Google Scholar] [Publisher Link]
  33. E. Ardizzone, H. Dindo, and G. Mazzola, “Restoration of Digitized Damaged Photos using Bit-Plane Slicing,” 2007 IEEE International Conference on Multimedia and Expo, Beijing, China, pp. 1643-1646, 2007.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Haider Al-Mahmood, and Zainab Al-Rubayec, “Lossless Image Compression based on Predictive Coding and Bit Plane Slicing,” International Journal of Computer Applications, vol. 93, no. 1, pp. 1-6, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Zhaoxia Yin, Yinyin Peng, and Youzhi Xiang, “Reversible Data Hiding in Encrypted Images Based on Pixel Prediction and Bit-Plane Compression,” IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 2, pp. 992-1002, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  36. Zhe Zhang et al., “Learned Lossless Image Compression based on Bit Plane Slicing,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 27579-27588, 2024.
    [Google Scholar] [Publisher Link]