Citation :

Shaiba Akhter, Rupaban Subadar, Sushanta Kabir Dutta, "Performance Evaluation of a Deep Learning-based Optimal Image Compression Scheme in Two Distinct Wireless Models," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 335-346, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P124

Abstract

The need for dependable transmission of high-quality images across wireless communication networks has grown due to the quick expansion of multimedia applications, including web services, digital photography, and medical imaging. Wireless channels, however, are particularly vulnerable to multipath fading, which raises the Bit Error Rate (BER), causes data loss, and deteriorates system performance. Therefore, reliable transmission techniques and efficient image compression are essential to ensure high-quality image delivery in poor channel circumstances. In this regard, this manuscript proposes an effective deep learning-based image compression framework intended to maintain image quality during wireless transmission as a solution to this problem. This manuscript first examines an autoencoder-based optimum compression strategy. The proposed autoencoder compression scheme obtains PSNR values of 24.81 dB and 27.58 dB, low MSE values of 0.004 and 0.002, and compression ratios of 0.38 and 0.75, respectively, according to performance evaluation using the Microsoft COCO and CIFAR-10 datasets. Secondly, using different modulation techniques like QPSK and BPSK with and without diversity schemes, the compressed images are transmitted over simple and sophisticated wireless channel models, like the Rayleigh fading and Nakagami-m fading channel models, respectively. According to experimental data, diversity-assisted modulation achieves PSNR values between 43.39 dB and 55.18 dB, greatly improving visual fidelity and transmission dependability. The proposed method successfully strikes a balance between the effectiveness of compression and image quality, making it a viable option for wireless multimedia communication in difficult channel circumstances.

Keywords

Image Compression, Deep Learning, Autoencoder, Diversity, Selection Combining.

References

1. Shahzeb Hussain, Namrata Bhadri, and Md. Shaayan Hussain, “Advancements in Wireless Communication,” International Journal of Electronics and Communication Engineering, vol. 7, no. 9, pp. 1-4, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Mostafa Naseri et al., “Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability, Throughput, and Latency,” arXiv preprint, pp. 1-13, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Chengsi Liang et al., “Generative AI-Driven Semantic Communication Networks: Architecture, Technologies and Applications,” IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 1, pp. 27-47, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Junhui Li, and Xingsong Hou, “Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression,” arXiv preprint, pp. 1-12, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Venkata Sankirtana Guntuboina, and Nishtha Srivastava, “Efficient Image Data Compression Techniques: A Comprehensive Review and Comparative Study,” Research Square, pp. 1-11, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Joshua Rajah Devadason, and Aju D, “A Comprehensive Survey of Image Compression Methods: From Prediction Models to Advanced Techniques,” Multimedia Tools and Applications, vol. 84, pp. 38653-38691, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Murooj Khalid Ibraheem Ibraheem, Alexander Viktorovich Dvorkovich, and Israa M. Abdalameer Al-khafaji, “A Comprehensive Literature Review on Image and Video Compression: Trends, Algorithms, and Techniques,” Information Systems Engineering, vol. 29, no. 3, pp. 863-876, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
8. G.K. Wallace, “The JPEG Still Picture Compression Standard,” IEEE Transactions on Consumer Electronics, vol. 387, no. 1, pp. 18-34, 1992.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Akane Shoda, Tomo Miyazaki, and Shinichiro Omachi “JPEG Image Enhancement with Pre-Processing of Color Reduction and Smoothing,” Sensors, vol. 23, no. 21, pp.1-16, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
10. C. Christopoulos, A. Skodras, and T. Ebrahimi, “The JPEG2000 Still Image Coding System: An Overview,” IEEE Transactions on Consumer Electronics, vol. 46, no. 4, pp. 1103-1127, 2000.
    [CrossRef] [Google Scholar] [Publisher Link]
11. M.W. Marcellin et al., “An Overview of JPEG-2000,” Proceedings DCC 2000. Data Compression Conference, Snowbird, UT, USA, pp. 523-541, 2000.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Miao Zhang et al., “Joint Lossless Image Compression and Encryption Scheme based on CALIC and Hyperchaotic System,” Entropy, vol. 23, no. 8, pp. 1-29, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Nuzhat Mobeen, Channappa A, and Suresh B, “Image Compression Methods for Efficient Storage and Transmission,” World Journal of Advanced Research and Reviews, vol. 11, no. 1, pp. 265-278, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Manish I. Patel, Sirali Suthar, and Jil Thakar, “Survey on Image Compression using Machine Learning and Deep Learning,” 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, pp. 1103-1105, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Irina Marcheko et al., “Image Compression Research Based On Convolutional Autoencoder,” Information Control Systems and Technologies (IUST-ODESA-2025): Proceedings of the 13^th International Scientific and Practical Conference, Odessa, pp. 24-26, 2025.
    [Google Scholar] [Publisher Link]
16. Lucas Theis et al., “Lossy Image Compression with Compressive Autoencoders,” arXiv preprint, pp. 1-19, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Yixin Mei et al., “Learning-Based Scalable Image Compression with Latent-Feature Reuse and Prediction,” IEEE Transactions on Multimedia, vol. 24, pp. 4143-4157, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Zhengxue Cheng et al., “Deep Convolutional Autoencoder-based Lossy Image Compression,” 2018 Picture Coding Symposium (PCS), San Francisco, CA, USA, pp. 253-257, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
19. M. Aiswarya, Jwala Jose, and S. Sritha, “A Review on Image Compression Techniques,” International Journal of Innovative Research in Technology, vol. 11, no. 7, pp. 1-5, 2024.
    [Google Scholar] [Publisher Link]
20. Renuka Girish Deshpande, Lata L. Ragha, and Satyendra Kumar Sharma, “Video Quality Assessment through PSNR Estimation for Different Compression Standards,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 3, pp. 918-924, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Umme Sara, Morium Akter, and Mohammad Shorif Uddin, “Image Quality Assessment through FSIM, SSIM, MSE, and PSNR—A Comparative Study,” Journal of Computer and Communications, vol. 7, no. 3, pp. 8-18, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Vlad-Ilie Ungureanu, Paul Negirla, and Adrian Korodi, “Image-Compression Techniques: Classical and “Region-of-Interest-Based” Approaches Presented in Recent Papers,” Sensors, vol. 24, no. 3, pp. 1-27, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
23. COCO Dataset 2017, Kaggle. [Online]. Available: https://www.kaggle.com/datasets/sabahesaraki/2017-2017
24. Swasti Jain et al., “Object Detection using the COCO Dataset,” 2022 International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates, pp. 1-4, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Faris Keti, “Diversity Techniques in Wireless Communication Systems: A Review,” European Journal of Theoretical and Applied Sciences, vol. 1, no. 6, pp. 431-440, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Satyanand Choudhary, Surbhi Jha, and Prabhakar Sharma, “A Review of Diversity Techniques in Wireless Communication System,” Proceedings of National Conference on Recent Advances in Electronics and Communication Engineering, pp. 1-6, 2014.
    [Google Scholar] [Publisher Link]
27. Raghunath Mandal et al., “Performance Evaluation of Selection Combiner Under Different Wireless Mobile Fading Channels-A Review,” 2024 2^nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT), Dehradun, India, pp. 163-168, 2024. [CrossRef] [Google Scholar] [Publisher Link]
28. Osamah S. Badarneh et al., “Selection Combining Over the Extended Fading Channels,” IEEE Wireless Communications Letters, vol. 11, no. 4, pp. 722-726, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Jai Kumar et al., “A Review on 5G and Beyond Wireless Communication Channel Models: Applications and Challenges,” Physical Communication, vol. 67, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
30. T.S. Rappaport, Wireless Communications: Principles and Practice, 2/E, Pearson Education India, pp. 1-736, 2010.
    [Google Scholar] [Publisher Link]
31. V.A. Aalo, “Performance of Maximal-Ratio Diversity Systems in a Correlated Nakagami-Fading Environment,” IEEE Transactions on Communications, vol. 43, no. 8, pp. 2360-2369, 2002.
    [CrossRef] [Google Scholar] [Publisher Link]
32. Emil Björnson, and Luca Sanguinetti, “Rayleigh Fading Modelling and Channel Hardening for Reconfigurable Intelligent Surfaces,” IEEE Wireless Communications Letters, vol. 10, no. 4, pp. 830-834, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
33. Marvin K. Simon, and Mohamed-Slim Alouini, Digital Communication over Fading Channels, John Wiley & Sons, 2004.
    [CrossRef] [Google Scholar] [Publisher Link]
34. Neelam Srivastava, Arpita Johri, and Mudit Saxena, “Performance of Nakagami–m Fading Channel with Equal Gain Combining and Maximal Ratio Combining Diversity Techniques,” International Journal of Electronics Communication and Computer Engineering, vol. 3, no. 1, pp. 46-52, 2012.
    [Google Scholar] [Publisher Link]
35. I. S. Gradshteyn, and I. M. Ryzhik, Table of Integrals, Series, and Products, Academic Press, pp. 1-1206, 2014.
    [Google Scholar] [Publisher Link]
36. Samar Kumar Rony, Farhana Akter Mou, and Md. Moshiur Rahman, “Performance Analysis of OFDM Signal using BPSK and QPSK Modulation Techniques,” American Journal of Engineering Research, vol. 6, no. 1, pp. 108-117, 2017.
    [Google Scholar] [Publisher Link]
37. R. Devi, and H. Nishat, “Performance Evaluation of Digital Modulation Schemes BPSK, QPSK, QAM,” International Journal Engineering Technology, vol. 3, no. 2, pp. 71-74, 2017.
    [Google Scholar]