NO-REFERENCE QUANTITATIVE ASSESSMENT OF GENERALIZED CONTRAST FOR COMPLEX IMAGES

Authors

  • E. S. Yelmanova Lviv Polytechnic National University, Ukraine
  • Y. M. Romanyshyn Lviv Polytechnic National University; University of Warmia and Mazury in Olsztyn, Poland, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2017-3-17

Keywords:

Image processing, image quality assessment, contrast measurement, no reference metric, generalized contrast, complex image, histogram.

Abstract

Context. Nowadays the task of automatically measuring of image quality in real time is extremely relevant for the vast majority of practical applications. No-reference quantitative assessment of image quality is one of the most pressing and difficult problems of image processing. Generalized contrast is the most important quantitative characteristic which determines the objective quality of the image. Currently, the development of new effective methods of no-reference measuring of generalized contrast for complex image in automatic mode which have the level of computing costs, which are acceptable to implement the processing in real time, is one of the most urgent tasks of image pre-processing.

Objective. Development of new histogram-based method for no-reference measurement of generalized contrast of complex images on the basis of the mean value for all contrast values of all pairs of image elements (objects and background) for various definitions of contrast kernel.

Method. Analysis of known approaches to measurement of a local contrast of the image elements, of known methods of the quantitative assessment of generalized contrast of complex images as well of the results of experimental research for a series of complex real and test images allowed to reveal inherent patterns (accordance to basic requirements to the definition of contrast, the nature and the dynamic of contrast changes at the linear transformations of the brightness scale), which are manifested depending on the use of the different definitions of the contrast kernels and the metrics of generalized contrast of images.

Results. New histogram-based method for no-reference measurement of generalized contrast for complex images is proposed. No-reference contrast metrics for the histogram-based measuring of generalized contrast of complex images on the basis of the average contrast of image elements for different definitions of contrast kernel is proposed.

Conclusions. Proposed no-reference metrics on the basis of the average contrast of image elements for proposed contrast kernels allow providing accurate quantitative assessment of generalized contrast of the real complex images and enable to evaluate (predict) with reasonable accuracy the perceived image quality at carrying out of subjective (qualitative) expert estimates.

Author Biographies

E. S. Yelmanova, Lviv Polytechnic National University

Post-graduate student

Y. M. Romanyshyn, Lviv Polytechnic National University; University of Warmia and Mazury in Olsztyn, Poland

Dr.Sc., Professor, Head of Department of Electronics and Information Technology, Lviv, Ukraine;

Professor of Department, Olsztyn, Poland

References

Wang Z., Bovik A. C., Sheikh H. R., Simoncelli E. P. Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, 2004, Vol. 13, No. 4, pp. 600–612. DOI: 10.1109/TIP.2003.819861.

Wang Z., Bovik A. C., Lu L. Why is image quality assessment so difficult?, IEEE International Conference on Acoustics, Speech and Signal Processing, Orlando, FL, USA, May 2002, Vol. 4, pp. 3313–3316. DOI: 10.1109/ICASSP.2002.1004620.

Wang Z., Bovik A. A universal image quality index, Signal Processing Letters, 2002, Vol. 9, No. 3, pp. 81–84. DOI: 10.1109/ 97.995823.

Wang Z., Bovik A. C. Modern Image Quality Assessment. San Rafael, CA, USA, Morgan & Claypool, 2006, 160 p. DOI: 10.2200/S00010ED1V01Y200508IVM003.

Shahid M., Rossholm A., L vstr m B., Zepernick H.-J. Noreference image and video quality assessment: a classification and review of recent approaches, EURASIP Journal on Image and Video Processing, 2014, No. 1, pp. 1–32. DOI: 10.1186/1687- 5281-2014-40.

Nesteruk V. F., Porfireva N. N. Kontrastnyiy zakon vospriyatiya sveta, Optika i spektroskopiya, 1970, Vol. ХХІХ, vyp. 6, pp. 1138–1143.

Peli E. Contrast in Complex Images, Journal of the Optical Society of America A, October 1990, Vol. 7, No. 10, pp. 2032– 2040. DOI: 10.1364/JOSAA.7.002032.

Vorobel’ R. Logaryfmichna obrobka zobrazhen’. Kiev, Naukova dumka, 2012, 231 p.

Vorobel R., Berehulyak O. Image contrast and its connection with fuzzy logic, Proceedings of II International Workshop on Inductive Modeling (IWIM), Prague, Czech Republic, 2007, pp. 104–110. ISBN 978-80-01-03881-9.

Gonzalez R. C., Woods R. E. Digital Image Processing, 2nd Edition. NY, Prentice Hall, 2002, 976 p. ISBN13: 978-0201180756, ISBN10: 0201180758.

Pratt W.K. Digital Image Processing. New York, Chichester, John Wiley & Sons, second edition, 1993, 720 р. DOI: 10.1002/ 0471221325.

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How to Cite

Yelmanova, E. S., & Romanyshyn, Y. M. (2017). NO-REFERENCE QUANTITATIVE ASSESSMENT OF GENERALIZED CONTRAST FOR COMPLEX IMAGES. Radio Electronics, Computer Science, Control, (3), 152–160. https://doi.org/10.15588/1607-3274-2017-3-17

Issue

Section

Progressive information technologies