Strange Images in Remote Sensing and Their Properties




lossy image compression, strange images, rate-distortion curve, image complexity


Lossy image compression is used in many applications including remote sensing. Image size and number increase and this often leads to the necessity to apply image compression. In lossy compression, it is assumed that rate-distortion curves are monotonous functions and this assumption is put into basis of compression control. However, it has been shown recently that there are grayscale and color images called “strange” for which the rate-distortion curves are not monotonous. In this paper, we demonstrate that some remote sensing images can be strange as well and this takes place for JPEG and some other compression techniques. Analysis of properties for strange images using Spearman rank order correlation coefficient is carried out and it is shown that there several parameters characterizing image complexity that have a rather high correlation with probability that a given image is strange. For example, image entropy is one of such parameters.


Abramov, S.K., Lukin, V.V, Ponomarenko, N.N., Pogrebnyak, O.B.(2009). Entropy-like measure of background content for image retrieval and sorting in large databases. Telecommunications and Radio Engineering. 68(8), 667-675.

Bellard F. (2018). BPG Image format. Retrieved from:

Bondžulić, B., Stojanović, N., Petrović, V., Pavlović, B., Miličević, Z. (2021). Efficient prediction of the first just noticeable difference point for JPEG compressed images. Acta Polytechnica Hungarica. 18(8), 201-220.

Bondzulic, B., Bujakovic, D., Li, F., Lukin, V. (2022). On strange images with application to lossy image compression. Radioelectronic and Computer Systems, 4, 143-152.

Blanes, I., Magli, E. and Serra-Sagrista, J. (2014). A tutorial on image compression for optical space imaging systems. IEEE Geoscience Remote Sensing Magazine. 2(3), 8-26.

Christophe, E. (Eds.) (2011). Hyperspectral data compression tradeoff. Optical remote sensing, 9-29.

Hussain, A.J., Al-Fayadh, A., Radi, N. (2018). Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing. 300, 44-69.

Krivenko, S.S., Krylova, O., Bataeva, E., Lukin, V.V. (2018). Smart Lossy Compression of Images Based on Distortion Prediction. Telecommunications and Radio Engineering, 77(17), 1535-1554. Kussul, Kussul, N., Lavreniuk, M., Shelestov, A., Skakun, S. (2018). Crop inventory at regional scale in Ukraine: Developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European Journal of Remote Sensing. 51(1), 627-636. DOI: 10.1080/22797254.2018.1454265.

Li, F., Krivenko, S., Lukin, V. (2020). Two-step providing of desired quality in lossy image compression by SPIHT. Radioelectronic and computer systems. 94(2), 22-32.

Li, F., Lukin, V. (2022). Strange Images with Non-monotonous Rate-Distortion Curves in Lossy Image Compression. Proceedings of 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education, 1-6. DOI: 10.1109/ICISCAE55891.2022.9927685

Yuchieh, J., Jin, L., Hu, S., Katsavounidis, I., Li, Z., Aaron, A., Jay Kuo, C.-C. (2015). Experimental design and analysis of JND test on coded image/video. SPIE Optical Engineering + Applications. 9599, 324-334.

Lukin, V., Vasilyeva, I., Krivenko, S., Li, F., Abramov, S., Rubel, O., Vozel, B., Chehdi, K., Egiazarian, K. (2020). Lossy Compression of Multichannel Remote Sensing Images with Quality Control. Remote Sensing. 12(22), 3840. DOI: 10.3390/rs12223840.

Manga I., Garba E. J. and Ahmadu A. S. (2021). Lossless Image Compression Schemes: A Review. Journal of Scientific Research and Reports, 27(6), 14-22. Doi: 10.9734/jsrr/2021/v27i630398.

Nan S., Feng X., Wu Y. and Zhang H. (2022). Remote sensing image compression and encryption based on block compressive sensing and 2D-LCCCM. Springer, 108, 2705-2729. Doi: 10.1007/s11071-022-07335-4.

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

Oh, H., Bilgin, A. and Marcellin, M. (2016). Visually lossless JPEG 2000 for remote image browsing. Information. 7(3), 1-45.

Ortega, A. and Ramchandran, K. (1998). Rate-distortion methods for image and video compression. IEEESignal Processing Magazine. 15(6), 23-50.

Ponomarenko, N.N., Lukin, V.V., Egiazarian, K., Astola, J. (2005). DCT Based High Quality Image Compression. Proceedings of 14th Scandinavian Conference on Image Analysis, 1177-1185. Lyalko, V., Popov, M., Sedlerova, O., Fedorovskyi, O., Stankevich, S., Yelistratova, L., Khyzhniak, A. (2022). On the development of remote sensing methods and technologies in Ukraine. Ukrainian journal of remote sensing, 9(2), 43-53. Doi:

Prasanna Y. L., Tarakaram Y., Mounika Y. and Subramani R. (2021). Comparison of Different Lossy Image Compression Techniques. 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1-7. Doi: 10.1109/ICSES52305.2021.9633800.

Sayood, K. (2017). Introduction to data compression. Morgan Kaufmann.

Singh B. K. and Sinha G. R. (2022). Medical Image Processing. In book: Machine Learning in Healthcare. Doi: 10.1201/9781003097808-4.

Spasova G. and Boyachev I. (2022). A Method of Color Images Compression. 2021 International Conference on Biomedical Innovations and Applications (BIA), 111-114. Doi: 10.1109/BIA52594.2022.9831403.

Zabala A., Pons X., Diaz-Delgado R., Garcia F., Auli-Llinas F. and Serra-Sagrista J. (2006). Effects of JPEG and JPEG2000 Lossy Compression on Remote Sensing Image Classification for Mapping Crops and Forest Areas. 2006 IEEE International Symposium on Geoscience and Remote Sensing, 790-793. DOI: 10.1109/IGARSS.2006.203.






Techniques for Earth observation data acquisition, processing and interpretation