Lossy compression of multilook SAR images in the optimal operation point neighborhood by BPG-coder

Vladimir Lukin, Volodymyr Rebrov, Andrii Pavliuk

Abstract


The subject matter of the article is the process of lossy compression of multilook Synthetic Aperture Radar (SAR) images corrupted by multiplicative, spatially correlated speckle noise, with a focus on operation in the neighborhood of the potential Optimal Operation Point (OOP). The goal of the article is to analyze the existence and properties of the OOP for SAR image compression using the Better Portable Graphics (BPG) coder, and to develop a practical method for achieving compression near this point. The tasks to be solved are: to verify the existence of the OOP for simulated Sentinel-1-like SAR images according to both traditional peak signal-to-noise ratio (PSNR) and visual quality (PSNR-HVS-M) metrics; to investigate the relationship between the compression control parameter (Q) and the resulting image quality and compression ratio (CR); and to propose and describe a practical iterative procedure for determining the Q parameter value corresponding to the OOP without requiring access to the noise-free reference image. The methods used are: simulation of SAR images with speckle relative variance equal to 0.05 using noise-free Sentinel-2 data as a reference; lossy compression using the BPG coder with parameter Q varying from 1 to 51; quantitative assessment using PSNR and PSNR-HVS-M metrics; calculation of compression ratio; analysis of rate-distortion curves between different image pairs; statistical estimation of equivalent noise variance for input PSNR prediction. The following results were obtained: It has been demonstrated that an OOP exists for the BPG coder when compressing multilook SAR images, confirmed by both PSNR and PSNR-HVS-M metrics. The OOP provides PSNR and PSNR-HVS-M values several dB higher compared to the uncompressed noisy image while achieving very high compression ratios (CR > 180). The OOP was found at high Q values (Q=48-49), where the coder aggressively suppresses noise but also introduces content distortions. A key practical result is the proposed method for determining Q at the OOP. Conclusions. The scientific novelty of the obtained results is as follows: For the first time, the existence of the OOP has been comprehensively demonstrated for the BPG coder applied to multilook SAR images with realistic speckle properties, considering not only the standard PSNR but also the visual quality metric PSNR-HVS-M, although the OOP is less pronounced for the latter; a method for practical OOP approximation has been developed, which operates without the need for the original noise-free (true) image, relying instead on an estimation of the speckle noise power from the available noisy data, making it applicable in real-world SAR image processing and transmission scenarios.

Keywords


synthetic aperture radar, lossy compression, speckled image, optimal operation point

Full Text:

PDF

References


Lysenko, A. SAR Data Spatial Resolution Enhancement for Environmental Monitoring Tasks. International Conference of Young Professionals «GeoTerrace-2023», 2023, vol. 2023, no. 1, pp. 1–5. DOI: 10.3997/2214-4609.2023510010.

Gao, W., Liu, Y., Zeng, Y., Liu, Q., & Li, Q. SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research. Sensors, 2023, vol. 23, no. 4. DOI: 10.3390/s23042266.

Asiyabi, R. M., Ghorbanian, A., Tameh, S. N., Amani, M., Jin, S. & Mohammadzadeh, A. Synthetic Aperture Radar (SAR) for Ocean: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, vol. 16, pp. 9106–9138. DOI: 10.1109/JSTARS.2023.3310363.

Okada, Y., Nakamura, S., Iribe, K., Yokota, Y., Tsuji, M., Tsuchida, M., Hariu, K., Kankaku, Y., Suzuki, S., Osawa, Y., & Shimada, M. System design of wide swath, high resolution, full polarimietoric L-band SAR onboard ALOS-2. 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013, pp. 2408–2411. DOI: 10.1109/IGARSS.2013.6723305.

Datcu, M., Schwarz, G., Schmidt, K., & Reck, C. Quality Evaluation of Compressed Optical and SAR Images: JPEG vs. Wavelets. Proc. of 1995 International Geoscience and Remote Sensing Symposium, IGARSS ’95, 1995, pp. 1687–1689. Available at: https://elib.dlr.de/23745/ (accessed 20 November 2025).

Kozhemiakin, R., Abramov, S., Lukin, V., Djurović, B., Djurović, I., & Simeunović, M. Strategies of SAR image lossy compression by JPEG2000 and SPIHT. 2017 6th Mediterranean Conference on Embedded Computing (MECO), 2017, pp. 1–6. DOI: 10.1109/MECO.2017.7977176.

Deng, J., & Huang, L. Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance. Remote Sensing, 2024, vol. 16, no. 5. DOI: 10.3390/rs16050891.

Rusyn, B., Lutsyk, O., Lysak, Y., Lukenyuk, A., & Pohreliuk, L. Lossless image compression in the remote sensing applications. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), 2016, pp. 195–198. DOI: 10.1109/DSMP.2016.7583539.

Yin, D., Gu, Z., Zhang, Y., Gu, F., Nie, S., Feng, S., Ma, J., & Yuan, C. Speckle noise reduction in coherent imaging based on deep learning without clean data. Optics and Lasers in Engineering, 2020, vol. 133, article no. 106151. DOI: 10.1016/j.optlaseng.2020.106151.

Sun, Z., Leng, X., Zhang, M., Ren, H., & Ji, K. SAR Image Object Detection and Information Extraction: Methods and Applications. Remote Sensing, 2025, vol. 17, no. 12. DOI: 10.3390/rs17122098

Ko, J., & Lee, S. SAR Image Despeckling Using Continuous Attention Module. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, vol. 15, pp. 3–19. DOI: 10.1109/JSTARS.2021.3132027.

Liu, Z., Wang, S., & Gu, Y. SAR Image Compression With Inherent Denoising Capability Through Knowledge Distillation. IEEE Geoscience and Remote Sensing Letters, 2024, vol. 21, pp. 1–5. DOI: 10.1109/LGRS.2024.3386758.

Ponomarenko, N. N., Lukin, V. V., Kozhemiakin, R. A., Egiazarian, K. O., & Chobanu, M. K. Lossy and visually lossless compression of single-look SAR images. Telecommunications and Radio Engineering, 2013, vol. 72, no. 8, pp. 711–729. DOI: 10.1615/TelecomRadEng.v72.i8.60.

Kryvenko, S., Lukin, V., & Vozel, B. Lossy Compression of Single-channel Noisy Images by Modern Coders. Remote Sensing, 2024, vol. 16, no. 12. DOI: 10.3390/rs16122093.

Odegard, J. E., Guo, H., Lang, M., Burrus, C. S., Jr., R. O. W., Novak, L. M., & Hiett, M. Wavelet-based SAR speckle reduction and image compression. SPIE's 1995 Algorithms for Synthetic Aperture Radar Imagery II 1995, vol. 2487, pp. 259–271. DOI: 10.1117/12.210843.

Yee, D., Soltaninejad, S., Hazarika, D., Mbuyi, G., Barnwal, R., & Basu, A. Medical image compression based on region of interest using better portable graphics (BPG). 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 216–221. DOI: 10.1109/SMC.2017.8122605.

Lainema, J., Hannuksela, M., Malamal Vadakital, V., & Aksu, E. HEVC still image coding and high efficiency image file format. 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 71–75. DOI: 10.1109/ICIP.2016.7532321.

Chen, Y., Mukherjee, D., Han, J., Grange, A., Xu, Y., Parker, S., Chen, C., Su, H., Joshi, U., Chiang, C.-H., Wang, Y., Wilkins, P., Bankoski, J., Trudeau, L., Egge, N., Valin, J.-M., Davies, T., Midtskogen, S., Norkin, A., & Liu, Z. An Overview of Coding Tools in AV1: the First Video Codec from the Alliance for Open Media. APSIPA Transactions on Signal and Information Processing, 2020, vol. 9. DOI: 10.1017/ATSIP.2020.2.

Bondzulic, B., Pavlović, B., Stojanovic, N., & Petrovic, V. Picture-wise just noticeable difference prediction model for JPEG image quality assessment. Military Technical Courier, 2022, vol. 70, pp. 62–86. DOI: 10.5937/vojtehg70-34739

Lukin, V., Kryvenko, S., & Pavliuk, A. Visually lossless compression of multilook SAR images. Aerospace Technic and Technology, 2025, pp. 123–133. DOI: 10.32620/aktt.2025.4.12.

Jamil, S. Review of Image Quality Assessment Methods for Compressed Images. Journal of Imaging, 2024, vol. 10, no. 5. DOI: 10.3390/jimaging10050113.

Nafchi, H., Shahkolaei, A., Hedjam, R., & Cheriet, M. Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator. IEEE Access, 2016, vol. 4, pp. 5579–5590. DOI: 10.1109/ACCESS.2016.2604042.

Reisenhofer, R., Bosse, S., Kutyniok, G., & Wiegand, T. A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment. Signal Processing Image Communication, 2018, vol. 61, pp. 33–43. DOI: 10.1016/j.image.2017.11.001.

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.

Rubel, O., Lukin, V., Rubel, A., & Egiazarian, K. Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images. Remote Sensing, 2021, vol. 13, no. 10. DOI: 10.3390/rs13101887.

Lukin, V., Kolganova, O., & Kryvenko, S. Lossy Compression of Images Corrupted by Spatially Correlated Noise. 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016, pp. 698-702. DOI: 10.1109/TCSET.2016.7452157.

Sentinel-1. Available at: https://sentinels.copernicus.eu/copernicus/sentinel-1 (accessed 20 November 2025).

Abdikan, S., Balik Sanli, F., Ustuner, M., & Calò, F. Land Cover Mapping Using SENTINEL-1 SAR Data. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, pp. 757–761. DOI: 10.5194/isprs-archives-XLI-B7-757-2016.

Fan, D., Zhao, T., Jiang, X., García-García, A., Schmidt, T., Samaniego, L., Attinger, S., Wu, H., Jiang, Y., Shi, J., Fan, L., Tang, B.-H., Wagner, W., Dorigo, W., Gruber, A., Mattia, F., Balenzano, A., Brocca, L., Jagdhuber, T., & Peng, J. A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment. Remote Sensing of Environment, 2025, vol. 318, article no. 114579. DOI: 10.1016/j.rse.2024.114579

Di Martino, G., Poderico, M., Poggi, G., Riccio, D., & Verdoliva, L. SAR image simulation for the assessment of despeckling techniques. 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 1797–1800. DOI: 10.1109/IGARSS.2012.6351163.

De Fioravante, P., Luti, T., Cavalli, A., Giuliani, C., Dichicco, P., Marchetti, M., Chirici, G., Congedo, L., & Munafò, M. Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification. Land, 2021 vol. 10, no. 6, article no. 611. DOI: 10.3390/land10060611.

Li, F., Ieremeiev, O., Lukin, V., & Egiazarian, K. BPG-Based Lossy Compression of Three-Channel Remote Sensing Images with Visual Quality Control. Remote Sensing, 2024, vol. 16, no. 15, article no. 2740. DOI: 10.3390/rs16152740.

Zemliachenko, A. N., Lukin, V. V., Ponomarenko, N. N., Egiazarian, K. O., & Astola, J. Still image/video frame lossy compression providing a desired visual quality. Multidimensional Systems and Signal Processing, 2016, vol. 27, no. 3, pp. 697–718. DOI: 10.1007/s11045-015-0333-8.




DOI: https://doi.org/10.32620/aktt.2025.6.05