On strange images with application to lossy image compression

Boban Bondzulic, Dimitrije Bujakovic, Fangfang Li, Vladimir Lukin

Abstract


Single and three-channel images are widely used in numerous applications. Due to the increasing volume of such data, they must be compressed where lossy compression offers more opportunities. Usually, it is supposed that, for a given image, a larger compression ratio leads to worse quality of the compressed image according to all quality metrics. This is true for most practical cases. However, it has been found recently that images are called “strange” for which a rate-distortion curve like dependence of the peak signal-to-noise ratio on the quality factor or quantization step, behaves non-monotonously. This might cause problems in the lossy compression of images. Thus, the basic subject of this paper are the factors that determine this phenomenon. The main among them are artificial origin of an image, possible presence of large homogeneous regions, specific behavior of image histograms. The main goal of this paper is to consider and explain the peculiarities of the lossy compression of strange images. The tasks of this paper are to provide definitions of strange images and to check whether non-monotonicity of rate-distortion curves occurs for different coders and metrics. One more task is to put ideas and methodology forward of further studies intended to detect strange images before their compression. The main result is that non-monotonous behavior can be observed for the same image for several quality metrics and coders. This means that not the coder but image properties determine the probability of an image to being strange. Moreover, both grayscale and color images can be strange, and both the natural scene and artificial images can be strange. This depends more on image properties than on image origin and number of channels. In particular, the percentage of pixels that belong to large homogeneous regions and image entropy play an important role. As conclusions, we outline possible directions of future research that, in the first order, relate to the analysis of images in large databases to establish parameters that show that a given image can be considered as strange.

Keywords


lossy compression; strange images; coders; quality metrics; rate-distortion curves

Full Text:

PDF

References


Zappavigna, M. Social media photography: construing subjectivity in Instagram images. Visual Communication, 2016, vol. 15, no. 3, pp. 271–292. DOI: 10.1177/1470357216643220.

Kussul, N., Lavreniuk, M., Shelestov, A., Skakun, S. 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, 2018, vol. 51, no. 1, pp. 627-636. DOI: 10.1080/22797254.2018.1454265.

Grgic, M., Kunt, M., Mrak, M. High-Quality Visual Experience: Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signals. Springer Berlin, Heidelberg Publ., 2010. 550 p. DOI: 10.1007/978-3-642-12802-8

Hussain, A. J., Al-Fayadh, A., Radi, N. Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing, 2018, vol. 300, pp. 44-69. DOI: 10.1016/j.neucom.2018.02.094.

Sayood, K. Introduction to data compression. 5th Edition, Morgan Kaufmann, San Francisco, 2017. 790 p.

Blanes, I., Magli, E., Serra-Sagrista, J. A tutorial on image compression for optical space imaging Systems. IEEE Geoscience and Remote Sensing Magazine, 2014, vol. 2, no. 3, pp. 8-26. DOI: 10.1109/MGRS.2014.2352465.

Oh, H., Bilgin, A., Marcellin, M. Visually lossless JPEG 2000 for remote image browsing. Information, 2016, vol. 7, no. 3, article no. 45. DOI: 10.3390/info7030045.

Bondžulić, B., Stojanović, N., Petrović, V., Pavlović, B., Miličević, Z. Efficient prediction of the first just noticeable difference point for JPEG compressed images. Acta Polytechnica Hungarica, 2021, vol. 18, no. 8, pp. 201-220. DOI: 10.12700/APH.18.8.2021.8.11.

Zabala, A., Pons, X. et al. Effects of JPEG and JPEG2000 lossy compression on remote sensing image classification for mapping crops and forest areas. IEEE International Symposium on Geoscience and Remote Sensing, 2006, pp. 790-793. DOI: 10.1109/IGARSS.2006.203.

Li, F., Krivenko, S., Lukin, V. Two-step providing of desired quality in lossy image compression by SPIHT. Radioelectronic and computer systems, 2020, no. 2(94), pp. 22-32. DOI: 10.32620/reks.2020.2.02.

Ozah, N., Kolokolova, A. Compression improves image classification accuracy. Proceedings of Canadian Conference on Artificial Intelligence. Lecture Notes in Computer Science, Springer, Cham, 2019, vol. 11489, pp. 525-530. DOI: 10.1007/978-3-030-18305-9_55.

Doss, S., Pal, S., Akila, D. et al. Satellite image remote sensing for identifying aircraft using SPIHT and NSCT. Journal of Critical Reviews, 2020, vol. 7, no. 5, pp. 631-634. DOI: 10.31838/jcr.07.05.130.

Zemliachenko, A., Ponomarenko, N., Lukin, V. et al. 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.

Lukin, V., Vasilyeva, I., Krivenko, S. et al. Lossy compression of multichannel remote sensing images with quality control. Remote Sensing, 2020, vol. 12, no. 22, article no. 3840. DOI: 10.3390/rs12223840.

Yang, K., Jiang, H. Optimized-SSIM based quantization in optical remote sensing image compression. Proceedings of Sixth International Conference on Image and Graphics, 2011, pp. 117-122. DOI: 10.1109/ICIG.2011.38.

Bondžulić, B., Pavlović, B., Stojanović, N., Petrović, V. Picture-wise just noticeable difference prediction model for JPEG image quality assessment. Vojnotehnički glasnik / Military Technical Courier, 2022, vol. 70, no. 1, pp. 62-84. DOI: 10.5937/vojtehg70-34739.

Llinàs, F. A. Model-based JPEG2000 rate control methods, PhD Thesis. Universitat Autònoma de Barcelona, 2006. 165 p.

Jeong, Y. W. et al. Rate distortion optimization encoding system and method of operating the same, US Patent, Patent No. 10,742,995 B2, 2020.

Ortega, A., Ramchandran, K. Rate-distortion methods for image and video compression. IEEE Signal Processing Magazine, 1998, vol. 15, no. 6, pp. 23-50. DOI: 10.1109/79.733495.

Li, F., Lukin,V., Liu, X. Strange images with non-monotonous rate-distortion curves in lossy image compression. Proceedings of Fifth International Conference on Information Systems and Computer Aided Education, 2022, pp. 11-15. DOI: 10.1109/ICISCAE55891.2022.9927685.

Kovalenko, B., Lukin, V., Kryvenko, S., Vozel, B. Prediction of parameters in optimal operation point for BPG-based lossy compression of noisy images. Ukrainian Journal of Remote Sensing, 2022, vol. 9, no. 2, pp. 4-12. DOI: 10.36023/ujrs.2022.9.2.212.

Corchs, S. E., Ciocca, G., Bricolo, E., Gasparini, F. Predicting complexity perception of real world images. PLoS ONE, 2016, vol. 11, no. 6, article no. e0157986. DOI: 10.1371/journal.pone.0157986.

Jin, L. et al. Statistical study on perceived JPEG image quality via MCL-JCI dataset construction and analysis. Proceedings of IS&T International Symposium on Electronic Imaging : Image Quality and System Performance XIII, 2016, vol. 28, article no. art00026, pp. 1-9. DOI: 10.2352/ISSN.2470-1173.2016.13.IQSP-222.

Makarichev, V., Lukin, V., Brysina, I. On estimates of coefficients of generalized atomic wavelets expansions and their application to data processing. Radioelectronic and computer systems, 2020, no. 1(93), pp. 44-57. DOI: 10.32620/reks.2020.1.05.

Melnyk, R., Tushnytskyy, R., Kvit, R. Cloudiness Images Multilevel Segmentation by Piecewise Linear Approximation of Cumulative Histogram. International Journal of Computing, 2020, no. 19(2), pp. 199-207. DOI: 10.47839/ijc.19.2.1762.




DOI: https://doi.org/10.32620/reks.2022.4.11

Refbacks

  • There are currently no refbacks.