ADVANTAGES AND DRAWBACKS OF TWO-STEP APPROACH TO PROVIDING DESIRED PARAMETERS IN LOSSY IMAGE COMPRESSION

Main Article Content

Fangfang Li
Sergey Abramov
Ihor Dohtiev
Vladimir Lukin

Abstract

The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of the two-step approach to lossy compression, to analyze the reasons of drawbacks, and to put forward possible ways to get around these shortcomings. Methods used: linear approximation, numerical simulation, statistical analysis. Results obtained: 1) the considered approach main advantage is that, in most applications, it provides substantial improvement of accuracy of providing a desired value of a controlled compression parameter after the second step compared to the first step; 2) the approach is quite universal and can be applied for different coders and different parameters of lossy compression to be provided; 3) the main problems and limitations happen due to the use of linear approximation and essential difference in behavior of rate/distortion curves for images of different complexity; 4) there are ways to avoid the approach drawbacks that employ adaptation to image complexity and/or use certain restrictions at the second step. Conclusions: based on the results of the study, it is worth 1) considering more complex approximations of rate-distortion curves; 2) paying more attention to adequate and fast algorithms of characterizing image complexity before compression; 3) using quality metrics that have quasi-linear rate/distortion curves for a given coder. 

Article Details

How to Cite
Li , F. ., Abramov , S. ., Dohtiev , I. ., & Lukin , V. (2024). ADVANTAGES AND DRAWBACKS OF TWO-STEP APPROACH TO PROVIDING DESIRED PARAMETERS IN LOSSY IMAGE COMPRESSION. Advanced Information Systems, 8(1), 57–63. https://doi.org/10.20998/2522-9052.2024.1.07
Section
Information systems research
Author Biographies

Fangfang Li , Nanchang Hang-kong University, Nanchang

Doctor of Philosophy, lecturer of Jiangxi Engineering Laboratory for Optoelectronics Testing Technology

Sergey Abramov , National Aerospace University, Kharkiv

Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Information and Communication Technology

Ihor Dohtiev , National Aerospace University, Kharkiv

postgraduate student of the Department of Information and Communication Technology

Vladimir Lukin , National Aerospace University, Kharkiv

Doctor of Technical Sciences, Professor, Head of the Department of Information and Communication Technology

References

Suetens, P. (2017), Fundamentals of medical imaging, Third edition, Cambridge University Press, Cambridge, 257 p., available at: https://www.amazon.com/Fundamentals-Medical-Imaging-Paul-Suetens/dp/0521519152

Stankevich, S.A. and Gerda, M.I. (2020), “Small-size target’s automatic detection in multispectral image using equivalence principle”, Central European Researchers Journal, Vol. 6(1), pp. 1–9, available at:

https://ceres-journal.eu/download.php?file=2020_01_01.pdf

Joshi, N, Baumann, M, Ehammer, A, Fensholt, R, Grogan, K, Hostert, P, Jepsen, M, Kuemmerle, T, Meyfroidt, P, Mitchard, E. and Waske, B. (2016), “A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring” Remote Sensing, vol. 8(1), 70, doi: https://doi.org/10.3390/rs8010070

Bataeva, E. and Chumakova-Sierova, A. (2022), “Values in Visual Practices of Instagram Network Users”, Integrated Computer Technologies in Mechanical Engineering, Nechyporuk M, Pavlikov V, Kritskiy D Eds. Lecture Notes in Networks and Systems; Springer International Publishing: Cham. 2022; vol. 367, 273869, pp. 992–1002, available at: https://link.springer.com/chapter/10.1007/978-3-030-94259-5_76

Radosavljević, M., Brkljač, B., Lugonja, P., Crnojević, V., Trpovski, Ž., Xiong, Z. and Vukobratović, D. (2020), “Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study”, Remote Sensing, vol. 12, 1590, doi: https://doi.org/10.3390/rs12101590

Zemliachenko, A., Kozhemiakin, R., Uss, M., Abramov, S., Ponomarenko, N., Lukin, V., Vozel, B. and Chehdi, K. (2014), “Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform”, Journal of Applied Remote Sensing, vol. 8(1), 25, doi: https://doi.org/10.1117/1.JRS.8.083571

Vasil'eva, I. and Popov, A. (2016), “An algorithm for recognition of hydrometeors by polarimetric radar data based on the information theory”, 2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW), pp. 1-4, doi: https://doi.org/10.1109/MSMW.2016.7538144

Blanes, I., Magli, E. and Serra-Sagrista, J. (2014), “A Tutorial on Image Compression for Optical Space Imaging Systems”, IEEE Geosci. Remote Sens. Mag., 2014; vol. 2, issue 1, pp. 8–26, doi: https://doi.org/10.1109/MGRS.2014.2352465

Hussain, J.A., Al-Fayadh, A. and Radi, N. (2018), “Image compression techniques: A survey in lossless and lossy algorithms”, Neurocomputing, vol. 300, pp. 44–69, doi: https://doi.org/10.1016/j.neucom.2018.02.094

Bondžulić, B., Stojanović, N., Petrović, V., Pavlović, B. and Miličević, Z. (2021), “Efficient Prediction of the First Just Noticeable Difference Point for JPEG Compressed Images”, Acta Polytechnica Hungarica, vol. 18(8), pp. 201–220, doi: https://doi.org/10.12700/APH.18.8.2021.8.11

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

Zemliachenko, A., Lukin, V., Ponomarenko, N., Egiazarian, K. and Astola, J. (2016), “Still image/video frame lossy compression providing a desired visual quality”, Multidim Syst Sign Process, vol. 27(3), pp. 697–718, doi: https://doi.org/10.1007/s11045-015-0333-8

Ozah, N. and Kolokolova, A. (2019), “Compression Improves Image Classification Accuracy. In Advances in Artificial Intelligence”, Meurs MJ, Rudzicz F Eds., Lecture Notes in Computer Science, Springer International Publishing, Cham., vol. 11489, pp. 525–530, doi: https://doi.org/10.1007/978-3-030-18305-9_55

Blau, Y. and Michaeli, T. (2019), “Rethinking lossy compression: The rate-distortion-perception tradeoff”, International Conference on Machine Learning, PMLR, pp. 675–685, doi: https://doi.org/10.48550/arXiv.1901.07821

Bondzulic, B., Bujakovic, D., Li, F. and Lukin, V. (2022), “On strange images with application to lossy image compression”, Radioelectronic and Computer Systems, No 4 (2022), Kharkiv, KhAI, pp. 143–152, doi: https://doi.org/10.32620/reks.2022.4.11

Li, F. and Lukin, V. (2023), “Providing a Desired Compression Ratio for Better Portable Graphics Encoder of Color Images: Design and Analysis, Digitalization and Management Innovation”, Proceedings of DMI 2022, IOS Press, pp. 633–640, doi: https://doi.org/10.3233/FAIA230063

Zemliachenko, A., Kozhemiakin, R., Abramov, S., Lukin, V., Vozel, B., Chehdi, K. and Egiazarian, K. (2018), “Prediction of compression ratio for DCT-based coders with application to remote sensing images”, Journal on Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11, No 1, pp. 257–270, doi: https://doi.org/10.1109/JSTARS.2017.2781906

Helin, H., Tolonen, T., Ylinen, O., Tolonen, P., Näpänkangas, J. and Isola, J. (2018), “Optimized JPEG 2000 Compression for Efficient Storage of Histopathological Whole-Slide Images”, Journal of pathology informatics, vol. 9, 20, doi: https://doi.org/10.4103/jpi.jpi_69_17

Bellard, F. (2024), BPG image format, available at: http://bellard. org/bpg/

Wu, D., Tan, D.M. and Wu, H.R. (2003), “Visually lossless adaptive compression of medical images”, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia Proceedings of the 2003 Joint [Internet], Singapore IEEE, 2003 [cited 2023 Sep 4], pp. 458–463, available at: http://ieeexplore.ieee.org/document/1292494/

Ye, N., Perez-Ortiz, M. and Mantiuk, R.K. (2019), “Visibility Metric for Visually Lossless Image Compression”, 2019 Picture Coding Symposium (PCS) [Internet], Ningbo, China IEEE; 2019 [cited 2023 Sep 4], pp. 1–5, available at: https://ieeexplore.ieee.org/document/8954560/

Ponomarenko, N. N., Lukin, V. V., Egiazarian, K. and Astola, J. (2005), “DCT Based High Quality Image Compression”, Proceedings of 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, pp. 1177–1185, doi: https://doi.org/10.1007/11499145_119

Ponomarenko, N.N., Egiazarian, K.O., Lukin, V.V. and Astola, J.T. (2007), “High-Quality DCT-Based Image Compression Using Partition Schemes”, IEEE Signal Process Lett, vol. 14(2), pp. 105–108, doi: https://doi.org/10.1109/LSP.2006.879861

Bondžulić, B.P., Pavlović, B.Z., Stojanović, N.M. and Petrović V.S. (2022), “Picture-wise just noticeable difference prediction model for JPEG image quality assessment”, Vojnotehnicki glasnik, Military Technical Courier, vol. 70(1), pp. 62–86, doi: https://doi.org/10.5937/vojtehg70-34739

Li, F., Lukin, V., Okarm,a K. and Fu, Y. (2021), “Providing a Desired Quality of BPG Compressed Images for FSIM Metric”, Proceedings of ATIT, Kyiv, Ukraine, pp. 10–14, doi: https://doi.org/10.1109/ATIT54053.2021.9678522

Wang, Z., Simoncelli, E. P. and Bovik, A. C. (2003), “Multiscale structural similarity for image quality assessment”, The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, vol. 2, pp. 1398–1402, doi: https://doi.org/10.1109/ACSSC.2003.1292216

Zhang, L., Zhang, L., Mou, X. and Zhang, D. (2011), “FSIM: A feature similarity index for image quality assessment”, IEEE Trans. Image Process, vol. 20(8), pp. 2378–2386, doi: https://doi.org/10.1109/TIP.2011.2109730

Li, F., Krivenko, S. and Lukin, V. (2020), „A Two-Step Procedure for Image Lossy Compression by ADCTC with a Desired Quality”, Proceedings of DESSERT2020, Ukraine, pp. 307–312, doi: https://doi.org/10.1109/DESSERT50317.2020.9125000

Li, F. (2022), Design and analysis of efficient methods for providing a desired quality in lossy image compression, The thesis for a degree of Doctor of Philosophy (PhD) in the field of knowledge 17 Electronics and Telecommunications in specialty 172 Telecommunications and radio engineering, National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, 2022, available at: https://khai.edu/assets/files/nauka/specradi/Thesis-of-Liff_2023_6_Last.pdf

Li, F., Krivenko, S. and Lukin, V. (2020), „Two-step providing of desired quality in lossy image compression by SPIHT“, Radioelectronic and computer systems, KhAI, Kharkiv, No. 2(96), pp. 22–32, doi: https://doi.org/10.32620/reks.2020.2.02

Li, F., Krivenko, S.S. and Lukin, V.V. (2020), “Analysis of two-step approach for compressing texture images with desired quality”, Aerospace Engineering and Technology, No. 1 (161), pp. 50–58, doi: https://doi.org/10.32620/aktt.2020.1.08

Li, F., Krivenko, S. and Lukin, V. (2020), “An Approach to Better Portable Graphics (BPG) Compression with Providing a Desired Quality”, 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, pp. 13–17, doi: https://doi.org/10.1109/ATIT50783.2020.9349289

Li, F., Lukin, V.V., Okarma, K., Fu, Y. and Duan, J. (2021), “Intelligent lossy compression method of providing a desired visual quality for images of different complexity”, Proceedings of AMMCS, China, pp. 500–505, doi: https://doi.org/10.1109/TCSET49122.2020.235483

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, vol. 4, pp. 5579–5590, doi: https://doi.org/10.1109/ACCESS.2016.2604042

Gu, K., Li, L., Lu, H., Min, X. and Lin, W. (2017), “A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures”, IEEE Trans. Ind. Electron, vol. 64, pp. 3903–3912, doi: https://doi.org/10.1109/TIE.2017.2652339

Reisenhofer, R., Bosse, S., Kutyniok, G. and Wiegand, T. (2018), ”Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment”, Signal Processing: Image Communication, vol. 61, pp. 33–43, doi: https://doi.org/10.1016/j.image.2017.11.001