Prediction of Parameters in Optimal Operation Point for BPG-based Lossy Compression of Noisy Images




image lossy compression, optimal operation point, quality prediction, noise, discrete cosine transform


Lossy compression of images corrupted by noise has several peculiarities. First, a specific noise filtering effect is observed. Second, optimal operation point (OOP) can be observed, i.e. such coder parameter (e.g., quantization step) value can exist that quality of compressed image calculated with respect to noise-free image can be better compared to quality of uncompressed (original noisy) image. If OOP exists, it is worth compressing a given image in OOP, if no, other recommendations on coder parameter setting are reasonable. Since noise-free image is not available in practice, it is not possible to determine does OOP exist and what is image quality in it. In this paper, we show that OOP existence for several quality metrics can be predicted quite easily and quickly for grayscale images corrupted by additive white Gaussian noise and compressed by better portable graphics (BPG) encoder. Such a prediction is based on analysis of statistics of discrete cosine transform (DCT) coefficients calculated for a limited number of 8x8 pixel blocks. A scatter-plot of metric improvement (reduction) depending upon these statistics is obtained in advance and prediction curve fitting is performed. Recommendations on encoder parameter setting for cases of OOP absence are given.


Abramov S., Krivenko S., Roenko A., Lukin V., Djurović I. and Chobanu M. (2013). Prediction of filtering efficiency for DCT-based image denoising. 2013 2nd Mediterranean Conference on Embedded Computing (MECO), 97–100. DOI: 10.1109/MECO.2013.6601327.

Aiazzi B., Alparone L., Baronti S., Lastri C., Selva M. (2012). Spectral distortion in lossy compression of hyperspectral data. Journal of Electrical Computer Engineering, Article ID 850637, 8. DOI:

Albalawi U., Mohanty S. P. and Kougianos E. (2016). Energy-Efficient Design of the Secure Better Portable Graphics Compression Architecture for Trusted Image Communication in the IoT. 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 302–307. DOI: 10.1109/ISVLSI.2016.21.

Al-Shaykh O. K., Mersereau R. M. (1998). Lossy compression of noisy images. IEEE Transactions on Image Processing, 7(12), 1641–1652. DOI: 10.1109/83.730376.

Bataeva E. V. (2012). Flanering and video mania: Modern and postmodern visual practices. Voprosy Filosofii, 11, 61–68.

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. DOI: 10.12700/APH.18.8.2021.8.11

Braunschweig R., Kaden I., Schwarzer J., Sprengel C., Klose K. (2009). Image data compression in diagnostic imaging: International literature review and workflow recommendation. Rofo, 181(7), 629–636.

Cameron A. C. and Windmeijer F. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics, 77(2), 329–342. DOI: 10.1055/s-0028-1109341.

Chang S. G., Yu B., Vetterli M. (1997). Image denoising via lossy compression and wavelet thresholding. Proceedings of International Conference on Image Processing, 1, 604–607. DOI: 10.1109/ICIP.1997.647985.

Chatterjee P. and Milanfar P. (2010). Is Denoising Dead? In IEEE Transactions on Image Processing, 19(4), 895-911. DOI: 10.1109/TIP.2009.2037087.

Chi M., Plaza A., Benediktsson J. A., Sun Z., Shen J., Zhu Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104(11), 2207–2219. DOI: 10.1109/JPROC.2016.2598228.

Christophe E. (2011). Hyperspectral Data Compression Tradeoff. In: Prasad S., Bruce L., Chanussot J. (eds) Optical Remote Sensing. Augmented Vision and Reality. DOI: 10.1007/978-3-642-14212-3_2.

Colom M., Buades A., Morel J.-M. (2014). Nonparametric noise estimation method for raw images. J. Opt. Soc. Am., 31(4), 863–871. DOI: 10.1364/JOSAA.31.000863.

Doss S., Pal S., Akila D., Jeyalaksshmi S., Jabeen T. N., Suseendran G. (2020). Satellite image remote sensing for identifying aircraft using SPIHT and NSCT. IEEE Signal processing magazine, 7(5), 631–634.

Guruswami V., Zuckerman D. (2016). Robust Fourier and Polynomial Curve Fitting. 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS), 751–759. DOI: 10.31838/jcr.07.05.130.

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

Kovalenko B., Lukin V., Naumenko V., Krivenko S. (2021). Analysis of noisy image lossy compression by BPG using visual quality metrics. 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), 20–25. DOI: 10.1109/ATIT54053.2021.9678575.

Khorram S., van der WieleFrank C. F., Koch F. H., Nelson S. C., Potts M. D. (2016). Future Trends in Remote Sensing. Principles of Applied Remote Sensing, 277–285.

Krivenko S., Lukin V., Krylova O. (2019). Visually Lossless Compression of Dental Images. 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), 394–399.

Li F., Krivenko S., Lukin V. (2020). Adaptive two-step procedure of providing desired visual quality of compressed image. Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, 407–414. DOI: 10.1145/3443467.3443791.

Li F., Krivenko S., Lukin V. (2020). A Fast Method for Visual Quality Prediction and Providing in Image Lossy Compression by SPIHT. Proceedings of Conference on Integrated Computer Technologies in Mechanical Engineering–Synergetic Engineering, 17–29. DOI: 10.1007/ 978-3-030-66717-7_2.

Ma Y., Wu H., Wang L., Huang B., Ranjan R., Zomaya A., Jie W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51, 47–60. DOI: 10.1016/j.future.2014.10.029.

Mahanti N. K., Pandiselvam R., Kothakota A., Ishwarya P., Chakraborty S. K., Kumar M., Cozzolino D. (2021). Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis. Trends in Food Science & Technology, 120, 418–438. DOI: 10.1016/j.tifs.2021.12.021.

Naumenko V., Lukin V., Krivenko S., Kovalenko B. (2021). Lossy compression of single-channel images corrupted by additive white noise with performance prediction. Accepted to ICTM, 2021.

Pandey A., Saini B. S., Singh B., Sood N. J. M. (2020). Quality controlled ECG data compression based on 2D discrete cosine coefficient filtering and iterative JPEG2000 encoding. Measurement, 152, 107252. DOI: 10.1016/j.measurement.2019.107252.

Penna B., Tillo T., Magli E., Olmo G. (2007). Transform coding techniques for lossy hyperspectral data compression. IEEE Transactions on Geoscience Remote Sensing, 45(5), 1408–1421. DOI: 10.1109/TGRS.2007.894565.

Ponomarenko N., Lukin V., Astola J., Egiazarian K. (2015). Analysis of HVS-metrics’ properties using color image database TID2013. In International Conference on Advanced Concepts for Intelligent Vision Systems, 613–624. DOI: 10.1007/978-3-319-25903-1_53.

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

Said A., Pearlman W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243–250. DOI: 10.1109/76.499834.

Sayood K. (2017) Introduction to data compression, San Francisco: Morgan Kaufmann, 768. ISBN: 978-0-12-415796-5

Selva E., Kountouris A., Louet Y. (2021). K-Means Based Blind Noise Variance Estimation. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 1–7. DOI: 10.1109/VTC2021-Spring51267.2021.9449072.

Tao D., Di S., Liang X., Chen Z., Cappello F. (2018). Fixed-PSNR Lossy Compression for Scientific Data. 2018 IEEE International Conference on Cluster Computing (CLUSTER), 314-318. DOI: 10.48550/arXiv.1805.07384.

Taubman D. S., Marcellin M. W. (2013). JPEG2000: image compression fundamentals, standards, and practice. Retrived from DOI: 10.1007/ 978-1-4615-0799-4.

Wang Z., Simoncelli E. P., Bovik A. C. (2003). Multiscale structural similarity for image quality assessment. IEEE Asilomar Conference on Signals, Systems and Computers, 2, 1398–1402. DOI: 10.1109/ACSSC.2003.1292216.

Wei Z., Ngan K. N. (2009). Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain. IEEE Transactions on Circuits and Systems for Video Technology, 19(3), 337–346. DOI: 10.1109/TCSVT. 2009.2013518.

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). 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 216–221. DOI: 10.1109/SMC.2017. 8122605.

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

Zappavigna M. (2016). Social media photography: construing subjectivity in Instagram images. Visual Communication, 15(3), 271–292. DOI: 10.1177/1470357216643220.

Zemliachenko A., Abramov S., Lukin V., Vozel B., Chehdi K. (2015). Lossy Compression of Noisy Remote Sensing Images with Prediction of Optimal Operation Point Existence and Parameters. SPIE Journal on Advances in Remote Sensing, 9(1), 26. DOI: 10.1117/1.JRS.9.095066.

Zhai G. and Min X. (2020). Perceptual image quality assessment: a survey. Science China Information Sciences, 63, 1-52.






Fundamentals of remote sensing