Aerospace Technic and Technology

The subject of the article is to analyze the effectiveness of lossy image compression using a BPG encoder using visual metrics as a quality criterion. The aim is to confirm the existence of an operating point for images of varying complexity for visual quality metrics. The objectives of the paper are the following: to analyze for a set of images of varying complexity, where images are distorted by additive white Gaussian noise with different variance values, build and analyze dependencies for visual image quality metrics, provide recommendations on the choice of parameters for compression in the vicinity of the operating point. The methods used are the following: methods of mathematical statistics; methods of digital image processing. The following results were obtained. Dependencies of visual quality metrics for images of various degrees of complexity affected by noise with variance equal to 64, 100, and 196. It can be seen from the constructed dependence that a working point is present for images of medium and low complexity for both the PSNR-HVS-M and MS-SSIM metrics. Recommendations are given for choosing a parameter for compression based on the obtained dependencies. Conclusions. Scientific novelty of the obtained results is the following: for a new compression method using Better Portable Graphics (BPG), research has been conducted and the existence of an operating point for visual quality metrics has been proven, previously such studies were conducted only for the PSNR metric.The test images were distorted by additive white Gaussian noise and then compressed using the methods implemented in the BPG encoder. The images were compressed with different values of the Q parameter, which made it possible to estimate the image compression quality at different values of compression ratio. The resulting data made it possible to visualize the dependence of the visual image quality metric on the Q parameter. Based on the obtained dependencies, it can be concluded that the operating point is present both for the PSNR-HVS-M metric and for the MS-SSIM for images of medium and low complexity, it is also worth noting that, especially clearly, the operating point is noticeable at large noise variance values. As a recommendation, a formula is presented for calculating the value of the compression control parameter (for the case with the BPG encoder, it is the Q parameter) for images distorted by noise with variance varying within a wide range, on the assumption that the noise variance is a priori known or estimated with high accuracy.

lossy image compression; Better Portable Graphics; MS-SSIM; PSNR-HVS-M

Garcia-Salgado, B., Ponomaryov, V., Sadovnychiy, S., Reyes-Reyes, R. Efficient dimension reduction of hyperspectral images for big data remote sensing applications. J. of Applied Remote Sensing, 2020, vol. 14, no. 3, article no. 032611. DOI: 10.1117/1.JRS.14.032611.

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

Chen, J., Deng, Z., Wang, Y., Cheng, X., Ye, Y., Zhang, X., Han, J. Image Compression Algorithm Based on Time Series. Artificial Intelligence and Security, 2021, pp. 689-700. DOI: 10.1007/978-3-030-78609-0_58.

Christophe, E., Prasad, S., Bruce, L., M., Chanussot, J. Hyperspectral data compression tradeoff. Optical remote sensing, Berlin, Heidelberg, Springer, 2011, vol. 3, pp. 9-29. DOI: 10.1007/978-3-642-14212-3_2.

Zabala, A., Pons, X. Impact of lossy compression on mapping crop areas from remote sensing. International Journal of Remote Sensing, 2013, vol. 34, no. 8, pp. 2796-2813. DOI: 10.1080/01431161.2012.750772.

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.

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

Ponomarenko, N., Krivenko, S., Lukin, V., Egiazarian, K., Astola, J. Lossy Compression of Noisy Images Based on Visual Quality: a Comprehensive Study. EURASIP Journal on Advances in Signal Processing, article no. 976436, 2010. DOI: 10.1155/2010/976436.

Bellard, F. BPG Image format. Available at: https://bellard.org/bpg/. (accessed 27.09.2021).

Albalawi, U., Mohanty, S. P., Kougianos, E. A Hardware Architecture for Better Portable Graphics (BPG) Compression Encoder. IEEE International Symposium on Nanoelectronic and Information Systems (iNIS), 2015. DOI: 10.1109/iNIS.2015.12.

Baranik, V. V., Shiryaev, A. V. Metod kvadraturnogo szhatiya transformant veivlet-preobrazovaniya v dvumernom poliadicheskom prostranstve [Method of quadrature compression of a wavelet transform transformant in a two-dimensional polyadic space]. Suchasna spetsial'na tekhnika – Modern special technique, 2011, no. 2 (25), pp. 73-80.

Chatterjee, P., Milanfar, P. Is Denoising Dead? IEEE Transactions on Image Processing, 2010, vol. 19, Iss. 4, pp. 895-911. DOI: 10.1109/TIP.2009.2037087.

Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., Battisti, F., Carli, M. New full-reference quality metrics based on HVS. Proceedings of the 2nd International Workshop on Video Processing and Quality Metrics, 2006, pp. 1–4.

Ponomarenko, N., Silvestri, F., Egiazarian, K., Astola, J., Carli, M., Lukin, V. On between-coefficient contrast masking of DCT basis functions. Proceedings of the 3rd International Workshop on Video Processing and Quality Metrics for Consumer Electronics, VPQM 2007, Scottsdale, Arizona, USA, 25-26 January 2007 . 4 p.

Wang, Z., Simoncelli, E. P., Bovik, A. C. Multi-scale structural similarity for image quality assessment. Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, 2003, pp. 1398–1402.