The existing methods of quantitative assessment of the visual quality of digital images are studied. Among the main shortcomings of the studied methods, the following can be singled out. Most of them require a reference image, do not include all the components that affect visual quality and do not take into consideration the laws of human visual perception. It was decided to develop a method for quantitative assessment of the visual quality of images, which will work without a reference image and will take into account the regularities of human visual perception. There are characterized the main regularities of human visual perception, which are used in the development of the technique. A classification of the researched methods of quantitative assessment of image quality is proposed for structuring their analysis. It was decided to investigate methods of quantitative assessment of quality based on statistical analysis of image pixel intensities. There are described factors affecting the quality of images and methods of their control based on changes in the pixel intensity distribution histogram. A generalized expression of quantitative quality assessment based on moments is proposed. A methodology for quantitative assessment of the visual quality of images has been developed, which does not require a reference image and is based on the laws of human visual perception. This method was tested on an image processed by local contrast enhancement and low-pass filtering. The test results showed that the visual perception of image quality coincides with the quantitative assessment of its quality. It is possible to use the proposed method with some modifications to determine the quality of color images. Moreover, a potential avenue for advancing the proposed method involves adapting it for evaluating images afflicted by distortions induced by noise presence.
1. Воробель, Р. А., & Журавель, І. М. (2001). Кількісна оцінка якості зображень. У: Праці. IV Середньоєвропейська конференція "Комп'ютерні методи та системи в автоматиці та електротехніці". Частина 1. Ченстохова, Польща, 17‑18 вересня, 2001.
2. Mason, A., Rioux, J., Clarke, S. E., Costa, A., Schmidt, M., Keough, V., Huynh, T., & Beyea, S. (2020). Comparison of Objective Image Quality Metrics to Expert Radiologists' Scoring of Diagnostic Quality of MR Images. IEEE Transactions on Medical Imaging, 39(4) . https://doi.org/10.1109/tmi.2019.2930338
3. Воробель, Р. А., Журавель, І. М., Опир, Н. В., Попов, Б. О., Дереча, В. Я., & Равлик, Я. М. (2000). Метод кількісної оцінки якості рентгенографічних зображень. У: Третя Українська науково-технічна конференція "Неруйнівний контроль та технічна діагностика – 2000", 233–236.
4. Boutros, F., Fang, M., Klemt, M., Fu, B., & Damer, N. (2023). CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR2023). https://doi.org/10.1109/CVPR52729.2023.00565
5. Jinjin, G., Haoming, C., Haoyu, C., Xiaoxing, Y., Ren, J. S., Chao, D. (2020). PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science (), vol 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_37
6. Madhusudana, P. C., Birkbeck, N., Wang, Y., Adsumilli, B., & Bovik, A. C. (2022). Image Quality Assessment Using Contrastive Learning. In: IEEE Transactions on Image Processing, 31, 4149–4161. https://doi.org/10.1109/TIP.2022.3181496
7. Zhang, W., Ma, K., Zhai, G., & Yang, X. (2021). Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild. In: IEEE Transactions on Image Processing, 30, 3474-3486. https://doi.org/10.1109/TIP.2021.3061932
8. Testolina, Michela, Lazzarotto, Davi, Rodrigues, Rafael, Mohammadi, Shima, Ascenso, João, Pinheiro, António M. G., & Ebrahimi, Touradj. (2023). On the Performance of Subjective Visual Quality Assessment Protocols for Nearly Visually Lossless Image Compression. Proceedings of the 31st ACM International Conference on Multimedia, October 2023, 6715-6723. https://doi.org/10.1145/3581783.3613835
9. Коваленко, Б. В., & Лукін, В. В. (2021). Використання візуальних метрик для аналізу стиснення з втратами зашумлених зображень. Авіаційно-космічна техніка і технологія, 6, 83–91. https://doi.org/10.32620/aktt.2021.6.09
10. Lukin, V., Bataeva, E., & Abramov, S. (2023). Saliency map in image visual quality assessment and processing. Radioelectronic and Computer Systems, 1(105), 112–121. https://doi.org/10.32620/reks.2023.1.09
11. Журавель, І. М. (2001). Локально-адаптивні методи підвищення контрастності зображень: Автореф. дис. канд. техн. наук: 05.13.06. НАН України. Держ. НДІ інформ. інфраструктури. Л., 19 с.