RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject matter is lossy compression using the BPG, AGU, AVIF, and HEIF encoders for medical images with different levels of visual complexity corrupted by additive Gaussian and Poisson noise. The goal of this study is to compare encoders regarding optimal image compression parameters and select the most suitable metric to determine the optimal operation point. The tasks considered include: selecting 512x512 grayscale test images with various degrees of visual complexity, including visually complex images rich in edges and textures, moderately complex images with edges and textures adjacent to homogeneous areas, and visually simple images consisting mainly of homogeneous areas; establishing image quality assessment metrics and evaluating their effectiveness under different encoder compression parameters; selecting one or more metrics that clearly determine the position of the optimal operation point; providing recommendations based on the results obtained for compressing medical images corrupted by additive white Gaussian and Poisson noises using four encoders to maximize the quality of the restored image to the noise-free original. The employed methods encompass image quality assessment techniques employing MSE, PSNR, and MSSIM metrics, as well as software modeling in Python without using the built-in Poisson noise generator. The results show that optimal operation points (OOPs) can be determined for all these metrics when the quality of the compressed image is better than the quality of the corresponding noisy original image, accompanied by a sufficiently high compression ratio. Moreover, achieving an appropriate balance between the compression ratio and image quality leads to partial noise reduction without noticeable information content distortion in the compressed image. This study emphasizes the importance of using appropriate metrics to assess the quality of compressed medical images and provides insight into the determination of the compression parameter Q to achieve the optimal operation point of the BPG encoder for specific images. However, the position of the OOP and its presence depend not only on the image complexity but also on the chosen encoder. Conclusions. The scientific novelty of the obtained results includes: 1) The consideration of noise models and parameter levels typical for medical imaging, namely, additive Gaussian noise of such intensity that it approximately corresponds to just noticeable differences, and signal-dependent Poisson noise; 2) The analysis of the multi-scale structural similarity index (MS-SSIM), which has not been previously explored in studies on lossy compression of noisy medical images; 3) A detailed examination of AVIF and HEIF coders to determine whether the optimal operating point (OOP) is observed for them and under which noise conditions; 4) The use of a dataset comprising ten medical images of varying visual complexity, with generalized tendencies revealed for different structural types; 5) The identification of the ability of many metrics to exhibit an OOP for images of moderate visual complexity or those dominated by homogeneous areas; 6) For Poisson noise, the demonstration of a dependence between the quality factor Q in the OOP and the average image intensity, which can be practically estimated for a given image; 7) The finding that different encoders require different approaches to determine their respective OOPs due to their distinct compression control parameters; 8) The observation that compression ratios achieved at the OOP are generally high, supporting the feasibility of using the OOP or its neighbourhood in practice.

lossy image compression; BPG; AGU; AVIF; HEIF; AWGN; Poisson noise; optimal operation point

Kim, M. E., Ramadass, K., Gao, C., Kanakaraj, P., Newlin, N., Rudravaram, G., Schilling, K. G., Dewey, B., Archer, D., Hohman, T. J., Li, Z., Bao, S., Landman, B. A., & Khairi, N. M. Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets. Imaging Informatics, San Diego, United States, 16–21 February 2025. SPIE, 2025. 23 p. DOI: 10.48550/ARXIV.2408.14611.

Sachenko, A., Yatskiv, V., Sieck, J., & Su, J. Image Transmission in WMSN Based on Residue Number System. International Journal of Computing, 2024, vol. 23, iss. 1, pp. 126–133. DOI: 10.47839/ijc.23.1.3444.

Yatskiv, V., Sachenko, A., Yatskiv, N., Bykovyy, P., & Segin, A. Compression and Transfer of Images in Wireless Sensor Networks Using the Transformation of Residue Number System. 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, pp. 1111–1114. DOI: 10.1109/IDAACS.2019.8924372.

Gopal, N., Kala, L., & Arun, L. Review on medical image compression. International Journal of Advanced Research in Science, Communication and Technology, 2023, pp. 54–64. DOI: 10.48175/ijarsct-12010.

Abduljaleel, I. Q., & Khaleel, A. H. Significant medical image compression techniques: a review. TELKOMNIKA (Telecommunication Computing Electronics and Control), 2021, vol. 19, iss. 5, article no. 1612. DOI: 10.12928/telkomnika.v19i5.18767.

Singh, M., Kumar, S., Singh, S., & Shrivastava, M. Various image compression techniques: lossy and lossless. International Journal of Computer Applications, 2016, vol. 142, iss. 6, pp. 23–26. DOI: 10.5120/ijca2016909829.

Rojas-Hernández, R., Díaz-de-León Santiago, J. L., Barceló-Alonso, G., Bautista-López, J., Trujillo-Mora, V., & Salgado-Ramírez, J. C. Lossless medical image compression by using difference transform. Entropy, 2022, vol. 24, iss. 7, article no. 951. DOI: 10.3390/e24070951.

Kryvenko, L., Krylova, O., Lukin, V., & Kryvenko, S. Intelligent visually lossless compression of dental images. Advanced Optical Technologies, 2024, article no. 13. DOI: 10.3389/aot.2024.1306142.

Kumar, S. N., Haridhas, A. K., Lenin Fred, A., & Sebastin Varghese, P. Analysis of lossy and lossless compression algorithms for computed tomography medical images based on bat and simulated annealing optimization techniques. In: Computational Intelligence Methods for Super-Resolution in Image Processing Applications. Cham: Springer International Publishing, 2021, pp. 99–133. DOI: 10.1007/978-3-030-67921-7_6.

Elsayed, H. A., Majeed, Q. E., & Sherbiny, M. M. E. Non-decimated wavelet transform and vector quantization for lossy medical images compression. Journal of Computer Science, 2023, vol. 19, iss. 3, pp. 363–371. DOI: 10.3844/jcssp.2023.363.371.

Cavaro-Menard, C., Le Callet, P., Barba, D., & Tanguy, J.-Y. Quality assessment of lossy compressed medical images. In: Compression of Biomedical Images and Signals. London, UK: ISTE, 2008, pp. 101–128. DOI: 10.1002/9780470611159.ch5.

Sultana, Z., Nahar, L., Tasnim, F., Hossain, M. S. & Andersson, K. Lossy compression effect on color and texture based image retrieval performance. In: Intelligent Computing & Optimization. Cham: Springer International Publishing, 2022, pp. 1159–1167. DOI: 10.1007/978-3-031-19958-5_108.

Kumar, D. V. The contemporary significance of image compression standards in medical imaging. Futuristic Trends in Medical Sciences, vol. 3, book 10. Iterative International Publisher, Selfypage Developers Pvt Ltd., 2024, pp. 138–163. DOI: 10.58532/v3bfms10p4ch3.

European Society of Radiology (ESR). Usability of irreversible image compression in radiological imaging: a position paper. Insights into Imaging, 2011, vol. 2, no. 2, pp. 103-115. DOI: 10.1007/s13244-011-0071-x.

Prasath, V. B. S. Quantum noise removal in X-ray images with adaptive total variation regularization. Informatica, 2017, vol. 28, no. 3, pp. 505–515. DOI: 10.15388/informatica.2017.141.

Rodrigues, I., Sanches, J., & Bioucas-Dias, J. Denoising of medical images corrupted by Poisson noise. Proceedings of the 15th IEEE International Conference on Image Processing, 12–15 October 2008, San Diego, CA, USA. IEEE, 2008. DOI: 10.1109/ICIP.2008.4712115.

Badgainya, S., Sahu, P. P., & Awasthi, P. V. Image denoising for AWGN corrupted image using OWT and thresholding. International Journal of Trend in Scientific Research and Development, 2018, vol. 2, no. 6, pp. 220–226. DOI: 10.31142/ijtsrd18338.

Kryvenko, S., & Lukin, V. Peculiarities of noisy image lossy compression. Herald of Khmelnytskyi National University. Technical Sciences, 2024, no. 333(2), pp. 278–283. DOI: 10.31891/2307-5732-2024-333-2-44.

Al-Shaykh, O. K., & Mersereau, R. M. Lossy compression of noisy images. IEEE Transactions on Image Processing, 1998, vol. 7, no. 12, pp. 1641–1652. DOI: 10.1109/83.730376.

Cheng, K. L., Xie, Y., & Chen, Q. Optimizing Image Compression via Joint Learning with Denoising. Lecture Notes in Computer Science, vol. 13679. Cham: Springer, 2022, pp. 56–73. DOI: 10.1007/978-3-031-19800-7_4.

Kovalenko, B., Lukin, V., Kryvenko, S., Naumenko, V., & Vozel, B. BPG-based automatic lossy compression of noisy images with the prediction of an optimal operation existence and its parameters. Applied Sciences, 2022, vol. 12, no. 15, article 7555. DOI: 10.3390/app12157555.

Kovalenko, B., Lukin, V., & Vozel, B. BPG-based lossy compression of three-channel noisy images with prediction of optimal operation existence and its parameters. Remote Sensing, 2023, vol. 15, no. 6, article 1669. DOI: 10.3390/rs15061669.

Bai, Y., Liu, X., Wang, K., Ji, X., Wu, X., & Gao, W. Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, vol. 46, no. 5, pp. 3577–3594. DOI: 10.1109/TPAMI.2023.3348486.

Makarichev, V., Lukin, V., & Brysina, I. Progressive DCT-based coder and its comparison to atomic function-based image lossy compression. Proceedings of the 16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 22–26 February 2022, Lviv-Slavske, Ukraine. IEEE, 2022. DOI: 10.1109/TCSET55632.2022.9766871.

Abramova, V., Kryvenko, S., Abramov, S., Makarichev, V., & Lukin, V. A fast procedure for image lossy compression by ADCTC using prediction of distortions’ MSE. In: Integrated Computer Technologies in Mechanical Engineering – 2022. Cham: Springer Nature Switzerland, 2023, pp. 625–635. DOI: 10.1007/978-3-031-36201-9_52.

Mansuri, I., & Bhatt, M. Image encryption and compression using discrete wavelet transform and adaptive thresholding. International Journal of Computer Science and Engineering, 2016, vol. 3, no. 11, pp. 48–54. DOI: 10.14445/23488387/IJCSE-V3I11P110.

Shahnaz, R., Walkup, J. F., & Krile, T. F. Image compression in signal-dependent noise. Applied Optics, 1999, vol. 38, no. 26, article no. 5560. DOI: 10.1364/AO.38.005560.

Kovalenko, B., Lukin, V., Kryvenko, S., Naumenko, V., & Vozel, B. BPG-based automatic lossy compression of noisy images with the prediction of an optimal operation existence and its parameters. Applied Sciences, 2022, vol. 12, iss. 15, article no. 7555. DOI: 10.3390/app12157555.

Wang, Z., Bovik, A. C., & Sheikh, H. R. Structural similarity based image quality assessment. In: Digital Video Image Quality and Perceptual Coding. CRC Press, 2017, pp. 225–242. DOI: 10.1201/9781420027822-7.

Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, 18–23 June 2018, pp. 586–595. DOI: 10.1109/CVPR.2018.00068.

Rudnichenko, N., Vychuzhanin, V., Vychuzhanin, A., Bercov, Y., Levchenko, A., & Otradskya, T. Adaptive Digital Image Compression. In: Digital Economy, Business Analytics, and Big Data Analytics Applications. Studies in Computational Intelligence, vol. 1010. Cham: Springer, 2022, pp. 45–53. DOI: 10.1007/978-3-031-05258-3_5.

Bellard, F. BPG image format. Available at: https://bellard.org/bpg/ (accessed 30 March 2025).

Ponomarenko, N. AGU image coder. Available at: https://www.ponomarenko.info/agu.htm (accessed 30 March 2025).

Awxkee. AVIF-Coder: Custom AVIF encoder for research and development. Available at: https://github.com/awxkee/avif-coder (accessed 30 March 2025).

Azzari, L., Borges, L. R., & Foi, A. Modeling and estimation of signal-dependent and correlated noise. In: Denoising of Photographic Images and Video. Cham: Springer International Publishing, 2018, pp. 1–36. DOI: 10.1007/978-3-319-96029-6_1.

Chen, M.-J., & Bovik, A. C. Fast structural similarity index algorithm. 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, PA, USA, 18–23 March 2005, vol. 2, pp. ii–998. DOI: 10.1109/ICASSP.2005.1449869.

Statistics Online. Lesson 28: Inferences for Two Population Means. Available at: https://online.stat.psu.edu/stat414/lesson/28/28.2 (accessed 19 March 2025)

Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., & Lukin, V. On between-coefficient contrast masking of DCT basis functions. Proceedings of the Third International Workshop on Video Processing and Quality Metrics, 2007, article 4.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, vol. 13, no. 4, pp. 600–612. DOI: 10.1109/TIP.2003.819861.