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  4. Generalized description of contrast of image elements

Generalized description of contrast of image elements

SISN.
2017;
Volume 872, Number 1
: pp. 35 - 42

Romanyshyn Y. M. Generalized description of contrast of image elements / Y. M. Romanyshyn, E. S. Yelmanova // Visnyk Natsionalnoho universytetu "Lvivska politekhnika". Serie: Informatsiini systemy ta merezhi. — Lviv : Vydavnytstvo Lvivskoi politekhniky, 2017. — No 872. — P. 35–42.

Authors: 

Romanyshyn Y. M., Yelmanova E. S.

  1. Lviv Polytechnic National University, Department of Electronics and Computer Technologies
  2. University of Warmia аnd Mazury in Olsztyn, Poland

The problem of image quality assessment in the transmission and processing of video data in information and telecommunication systems is considered. The task of no-reference measurement of contrast of two image elements (objects and background) for complex (multi-element) images is solved. A new method of contrast measurement of elements of complex images on the basis of analytical assessments of contrast of appropriate elements on initial (primary) and on inverse (negative) images is proposed. А generalized description of image elements contrast for different definition of contrast kernels is suggested. The new definitions of a weighted and relative contrast of image elements are proposed. The research of known and proposed definitions of a weighted and relative contrast to evaluate the efficiency of measuring (of quantitative assessment) of contrast of image elements was carried out.

image quality assessment
image contrast
contrast of image elements
weighted contrast
relative contrast
contrast kernel

1. Wang, Z. and Bovik, A. C. (2006), Modern Image Quality Assessment, Morgan & Claypool Publishers, San Rafael, California, USA.

2. Gonzalez, R.C. and Woods, R. E. (2002), Digital Image Processing, 2nd ed., Prentice Hall, New Jersey, USA.

3. Wang, Z. and Bovik, A. (2002). A universal image quality index, IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84.

4. Pratt, W. K. (2001), Digital Image Processing: PIKS Inside, 3rd ed., John Wiley & Sons, Inc., New York, USA.

5. Peli, E. (1990). Contrast in Complex Images, Journal of the Optical Society of America, A, vol. 7, no. 10, pp. 2032–2040.

6. Nesteruk, V. F. and Porfyryeva, N. N. (1970). Contrasting law of light perception, Optics and Spectroscopy, vol. KhKhIKh, no. 6, pp. 1138–1143 (In Russian).

7. Vorobel, R. A. (1999), Digital image processing based on the theory of contrast, D. Sc. Thesis, Lviv, Ukraine.

8. Vorobel, R. A. (2012). Logarithmic Image Processing, Naukova Dumka, Kyiv, Ukraine (In Ukrainian).

9. Vorobel, R. A. (2011). The generalized description of weighted and relative contrasts of elements of monochrome images, Information Extraction and Processing, issue 34 (110), pp. 120–128.

10. Yelmanova, E. (2016). Quantitative Evaluation of Contrast for a Complex Image by its Histogram, Proc. of 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science TCSET’2016, Lviv-Slavske, Ukraine, pp. 688–692.

11. Pratt, W. K. (1978), "Psychophysical properties of vision", in: Digital Image Processing, John Wiley & Sons, Inc., New York, USA, pp. 23–49.

12. Stockham, T. G. (1972). Image processing in the context of a visual model, Proc. of the IEEE, vol. 60,no. 7, pp. 828–442.

13. Jourlin, M., Pinoli, J-C. and Zeboudj R. (1989). Contrast definition and contour detection for logarithmic images, Journal of Microscopy, vol. 156, issue 1, pp. 33–40.

14. Public-Domain Test Images for Homework’s and Projects, available at: https://homepages.cae.wisc.edu/~ece533/images/lena.bmp (Accessed 1 December 2016).