PARAMETERS OF THE CURVE OF LOCAL ESTIMATE DISTRIBUTION AS IMAGE QUALITY MEASURES
https://doi.org/10.21122/2309-4923-2018-3-26-41
Abstract
This article focuses on selecting the best quality image from the series without the reference image. The results of studies of a new approach to digital image quality assessment, based on the local quality estimates distribution, are presented. One of the parameters of such a distribution is proposed to be used as a measure of image quality. 16 quality measures of the images described in the scientific literature have been selected. It is shown that the scale parameter of the Weibull distribution is a more accurate global quality measure for the set of local estimates than the mean value. A number of experiments have been carried out to confirm the correctness of such an estimate and its correlation with visual estimates of image quality. Such estimates are very important for a) quality assessment of automatically generated photographs, b) selection of parameters for enhancement-oriented image transformations, such as brightness changes, compression of the dynamic range of brightness, conversion to the grayscale representation, and others.
About the Authors
F. V. Starovoitov
Belarusian National Technical University
Belarus
Belarus
Master of Engineering
V. V. Starovoitov
United Institute of Informatics Problems of the NAS of Belarus
Belarus
Belarus
Professor, PhD in Engineering
References
1. Старовойтов, В. В. Локальные геометрические методы цифровой обработки и анализа изображений.— Минск: Ин-т техн. кибернетики НАН Беларуси, 1997. Starovoitov, V. V. Lokal’nye geometricheskie metody cifrovoj obrabotki i analiza izobrazhenij.— Minsk: In-t tehn. kibernetiki NAN Belarusi, 1997.
2. Caviedes, J. et al. Impairment metrics for digital video and their role in objective quality assessment // Visual Communications and Image Processing, Perth, Australia, 30 May 2000. – P. 791–800.
3. Lin, W., Kuo, C. C. J. Perceptual visual quality metrics: A survey // Journal of Visual Communication and Image Representation, 2011. – V. 22. – №. 4. – P. 297–312.
4. Mittal, A., Moorthy, A. K., Bovik, A. C. No-reference image quality assessment in the spatial domain // IEEE Transactions on Image Processing. – 2012. – V. 21. – №. 12. – P. 4695–4708.
5. Chandler, D. M. Seven challenges in image quality assessment: past, present, and future research // ISRN Signal Processing. – 2013. – V. 2013, 53p.
6. Manap, R. A. Shao L. Non-distortion-specific no-reference image quality assessment: A survey // Information Sciences, 2015. – V. 301. – P. 141–160.
7. Старовойтов В. В., Старовойтов Ф. В. Сравнительный анализ безэталонных мер оценки качества цифровых изображений // Системный анализ и прикладная информатика, 2017. – № 1. – С. 24–32.
8. Pertuz S., Puig D., Garcia M. A. Analysis of focus measure operators for shape-from-focus // Pattern Recognition, 2013. – V. 46. – № 5. – P. 1415–1432.
9. Старовойтов В. В. Сингулярное разложение матриц в анализе цифровых изображений // Информатика. 2017. № 2. C. 77–90.
10. Geusebroek J. M. The stochastic structure of images // Int. Conference on Scale-Space Theories in Computer Vision. – Springer Berlin Heidelberg, 2005. – С. 327–338.
11. Geusebroek J. M., Smeulders A. W. M. A six-stimulus theory for stochastic texture // International Journal of Computer Vision. – 2005. – Т. 62. – №. 1–2. – С. 7–16.
12. Scholte S. H., et al. Brain responses strongly correlate with Weibull image statistics when processing natural images // Journal of Vision, 2009. – V. 9. – № 4. – P. 29–44.
13. Xue W., Mou X. Reduced reference image quality assessment based on Weibull statistics // 2010 Second International Workshop on Quality of Multimedia Experience (QoMEX), Trondheim, 2010, pp. 1–6.
14. Статистические методы. Распределение Вейбулла. Анализ данных: ГОСТ Р 50779.27–2017. – Введ. 10.08.17. – Москва: Госстандарт России: Изд-во стандартов, 2017. – 62 с.
15. Lyon A. Why are Normal Distributions Normal? // The British Journal for the Philosophy of Science, 2014 – Т. 65. – Вып. 3. – С. 621–649.
16. База стандартных тестовых изображений: Signal and Image Processing Institute,
17. University of Southern California, CA. Режим доступа: http://sipi.usc.edu/database/database.php? volumemisc, 44 images, tiff. Дата доступа: 10.02.2017.
18. Гонсалес Р., Вудс Р. Цифровая обработка изображений. Издание 3-е, исправленное и дополненное. – Москва: Техносфера, 2012. – 1104 с.
Review
For citations:
Starovoitov F.V., Starovoitov V.V. PARAMETERS OF THE CURVE OF LOCAL ESTIMATE DISTRIBUTION AS IMAGE QUALITY MEASURES. «System analysis and applied information science». 2018;(3):26-41. (In Russ.) https://doi.org/10.21122/2309-4923-2018-3-26-41
Views:
1799
Email this article 