Block-Based Lossless Image Coding through Image Quality Improvement using the Prediction by Partial Matching Algorithm

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : P.R. Rajesh Kumar, M. Prabhakar

10.14445/23488549/IJECE-V12I12P104

How to Cite?

P.R. Rajesh Kumar, M. Prabhakar, "Block-Based Lossless Image Coding through Image Quality Improvement using the Prediction by Partial Matching Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 12, pp. 29-43, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P104

Abstract:

Applications requiring exact image reconstruction always need lossless image coding. A novel method of block based lossless image coding based on the use of the Prediction by Partial Matching (PPM) algorithm combined with two channel coding and adaptive Huffman coding is presented in this paper. In this work, images are segmented into non overlapping blocks, and pixels are efficiently predicted through the application of context modeling using PPM. To improve coding efficiency, a Two-channel coding is employed to separate bit and data streams. The encoded streams are further compressed by a Huffman coding scheme, adaptively adjusting symbol probabilities to local data statistics. The experimental results demonstrate an improvement in the compression ratio while maintaining image quality. Working with statistical and predictive models, the integration of PPM and two-channel and adaptive Huffman coding has created a flexible and robust coding framework. Finally, the proposed method is compared with previous state-of-the-art lossless coding techniques and evaluated in terms of compression efficiency as well as computational behavior, and found to be superior in both aspects. The proposed method demonstrates an average improvement of 46.97% in CR, 32.62% in BPP, and 2.05% in entropy compared to the TIFF, BMP, and LZW methods. This has shown a bright technology in high-fidelity image storage and transmission devices.

Keywords:

Adaptive Huffman Coding, Compression Ratio, Loss Image coding, Prediction by Partial Matching (PPM), Two Channel coding.

References:

[1] A.J. Hussain, Ali Al-Fayadh, and Naeem Radi, “Image Compression Techniques: A Survey in Lossless and Lossy Algorithms,” Neurocomputing, vol. 300, pp. 44-69, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Md. Atiqur Rahman, and Mohamed Hamada, “Lossless Image Compression Techniques: A State-of-the-Art Survey,” Symmetry, vol. 11, no. 10, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Yong Zhang, and Donald A. Adjeroh, “Prediction by Partial Approximate Matching for Lossless Image Compression,” IEEE Transactions on Image Processing, vol. 17, no. 6, pp. 924-935, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Muhammad Usama et al., “An Efficient Secure Data Compression Technique Based on Chaos and Adaptive Huffman Coding,” Peer-to Peer Networking and Applications, vol. 14, pp. 2651-2664, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Antonin Descampe et al., “JPEG XS-A New Standard for Visually Lossless Low-Latency Lightweight Image Coding,” Proceedings of the IEEE, vol. 109, no. 9, pp. 1559-1577, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jungan Chen et al., “Bit-Error Aware Lossless Image Compression with 2D-Layer-Block Coding,” Journal of Sensors, vol. 2021, pp. 1 18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Shinichi Yamagiwa, Wenjia Yang, and Koichi Wada, “Adaptive Lossless Image Data Compression Method Inferring Data Entropy by Applying Deep Neural Network,” Electronics, vol. 11, no. 4, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Tungshou Chen et al., “A High Fidelity Authentication Scheme for AMBTC Compressed Image using Reference Table Encoding,” Mathematics, vol. 9, no. 20, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Tassnim Dardouri et al., “Dynamic Neural Network for Lossy-to-Lossless Image Coding,” IEEE Transactions on Image Processing, vol. 31, pp. 569-584, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Yuanchao Bai et al., “Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 3577-3594, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Gangtao Xin, and Pingyi Fan, “Soft Compression for Lossless Image Coding Based on Shape Recognition, Entropy, vol. 23, no. 12, pp. 1-22, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Małgorzata Frydrychowicz, and Grzegorz Ulacha, “Two-Stage Golomb – Context-Adaptive Binary Arithmetic Coders Coding in Lossless Image Compression,” Advances in Science and Technology Research Journal, vol. 18, no. 6, pp. 62-85, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] M. Sri Raghavendra et al., “Enhancing Lossless Image Compression through Smart Partitioning, Selective Encoding, and Wavelet Analysis, International Journal of Electronics and Communication Engineering, vol. 11, no. 5, pp. 207-219, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Benjamin Lukas Cajus Barzen et al., “Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture,” 2022 Picture Coding Symposium (PCS), San Jose, CA, USA, pp. 109-113, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Xiaoxiao Liu e al., “An Improved Lossless Image Compression Algorithm Based on Huffman Coding,” Multimedia Tools and Applications, vol. 81, pp. 4781-4795, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Grzegorz Ulacha, Ryszard Stasinski, and Cezary Wernik, “Extended Multi WLS Method for Lossless Image Coding,” Entropy, vol. 22, no. 9, pp. 1-24, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Francisco Dantas Nobre Neto, Cláudio de Souza Baptista, and Claudio E.C. Campelo, “Combining Markov Model and Prediction by Partial Matching Compression Technique for Route and Destination Prediction,” Knowledge-Based Systems, vol. 154, pp. 81-92, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] F. Irem Eyiokur et al., “Exposure Correction Model to Enhance Image Quality,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA, pp. 676-686, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Phyela Mbewe, and Sampson D. Asare, “Analysis and Comparison of Adaptive Huffman Coding and Arithmetic Coding Algorithms,” 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China, pp. 178-185, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sijian Liang et al., “Joint Scrambling and Diffusion Image Encryption based on RNA Theory and Row–Column–Level Operation,” Journal of Electronic Imaging, vol. 33, no. 5, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Francisco F. Cunha et al., “Lossy Image Compression in a Preclinical Multimodal Imaging Study,” Journal of Digital Imaging, vol. 36, pp. 1826-1850, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Zhaoyang Jia et al., “Generative Latent Coding for Ultra-Low Bitrate Image Compression,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 26088-26098, 2024.
[CrossRef] [Google Scholar] [Publisher Link]