Granular Partitioning and Adaptive Encoding: A Synergistic Approach to Lossless Image Compression

Main Article Content

Zhang S
Fdez-Riverola F
S. Kiran

Abstract

Due to the ever-increasing need for efficient digital image storage and transmission, lossless compression techniques that preserve image integrity are crucial. However, achieving high compression ratios while maintaining quality remains a challenge. This paper addresses this gap by proposing Granular Partitioning and Adaptive Encoding, a novel approach for enhanced lossless image compression. Traditional methods often employ fixed-size image blocks, neglecting the inherent variability within images. Our approach tackles this by introducing content-adaptive partitioning, which intelligently segments images into smaller regions based on features like texture and smoothness. This allows for more targeted compression strategies. Furthermore, adaptive encoding applies different compression algorithms to distinct partitions based on their specific content. This ensures optimal compression for each image region. We evaluate the proposed framework using a comprehensive image dataset and compare its performance against existing state-of-the-art methods. This research demonstrates that the synergistic combination of granular partitioning and adaptive encoding significantly improves lossless compression ratios while maintaining image quality. This approach is applicable to various image processing tasks where efficient storage and transmission are essential. Performance is measured using metrics like compression ratio and computational complexity.

Article Details

How to Cite
[1]
Zhang S, Fdez-Riverola F, and S. Kiran, “Granular Partitioning and Adaptive Encoding: A Synergistic Approach to Lossless Image Compression”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 3, pp. 54–63, Mar. 2024.
Section
Research Articles

References

Alladi, T., & Lakshminarayanan, K. (2020). A survey on federated learning with applications. Journal of Network and Computer Applications, 166, 102709. https://www.sciencedirect.com/science/article/pii/S0950705121000381

Apostolopoulos, I., & Zoulias, E. (2021). Lightweight cryptography for resource-constrained iot devices: A survey. IEEE Communications Surveys & Tutorials, 23(2), 868-884. https://ieeexplore.ieee.org/document/9677225/

Chen, H., Zhao, Y., Li, X., Gong, L., Su, H., & Liu, X. (2.022). Quantum-resistant authenticated key exchange for the internet of things. IEEE Transactions on Information Forensics and Security, 17(1), 189-203. https://ieeexplore.ieee.org/iel7/6287639/6514899/09547310.pdf

Dang, V. T. (2019). Status report on the second round of the NIST post-quantum cryptography standardization process. National Institute of Standards and Technology (NIST) Interagency Report (NISTIR), 8104. https://www.nist.gov/publications/status-report-second-round-nist-post-quantum-cryptography-standardization-process

Gasser, T. (2018). Quantum computing threat landscape and mitigation strategies. Future of Security, 1(1), 16-25. https://www.mecs-press.org/ijwmt/ijwmt-v12-n5/IJWMT-V12-N5-2.pdf

Gupta, M., Patel, J., & Vaishnav, R. (2020). Federated learning for anomaly detection in iot systems. 2020 IEEE International Conference on Computational Intelligence and Virtual Environments (CIVE) (pp. 215-220). IEEE. https://ieeexplore.ieee.org/abstract/document/9394275

Huh, J. Y., Kim, S., & Kim, J. (2019). A lightweight blockchain-based federated learning for iot security. Sensors, 19(17), 3680. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635913/

Jang, Y., Kim, J., & Kim, S. (2020). A secure and efficient federated learning framework for iot using homomorphic encryption. IEEE Internet of Things Journal, 7(7), 6428-6437. https://ieeexplore.ieee.org/document/10208145

Jiang, Y., Li, M., Ding, W., & Lv, Z. (2020). Lightweight and privacy-preserving federated learning for iot devices. IEEE Transactions on Parallel and Distributed Systems, 32(3), 689-701. https://ieeexplore.ieee.org/document/8382158

Kim, H., Park, J., & Cheon, J. H. (2021). Post-quantum cryptography and the future of secure communication. Journal of Information Security, 12(1), 75-88. https://onlinelibrary.wiley.com/doi/full/10.1002/spe.3121

Li, X., Liang, X., Zhao, R., Sun, X., & Zhu, H. (2020). Federated learning with on-device filtering for privacy-preserving iot analytics. 2020 IEEE International Conference on Big Data (Big Data) (pp. 5432-5441). IEEE.

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236

Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419

Nagi, J., Ducatelle, F., Di Caro, G. A., Cireşan, D. C., Meier, U., Giusti, A., ... & Schmidhuber, J. (2011). Max-pooling convolutional neural networks for vision-based hand gesture recognition. In 2011 IEEE International Conference on Signal and Image Processing Applications (pp. 342-347). https://doi.org/10.1109/ICSIPA.2011.6144164

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191

Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR).

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9). https://doi.org/10.1109/CVPR.2015.7298594

Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1492-1500). https://doi.org/10.1109/CVPR.2017.634

Zhang, Z., Cui, P., & Zhu, W. (2018). Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2020.2981333

Zhou, Z. H., & Feng, J. (2017). Deep forest: Towards an alternative to deep neural networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 3553-3559). https://doi.org/10.24963/ijcai.2017/497