On properties of the block-based binarization algorithm of digital images
Abstract
Modern methods of the binarization which are widely used in printing industry by printing for the halftoning process are considered in the article. From the results of binarization grayscale image from the calculations the chosen distortion measures it has been found that binarized image created using block-based halftoning algorithm has the best results, than those results which were obtained by other halftoning algorithms. Also the goal of this work was experiment on printing tone scale and measurement of the reflectance coefficients of the halftones using spectrophotometer. Gradation curves for given printer model were obtained. It is founded, that for the block-based binarization algorithm increasing a contrast is observed, that confirmed by real printing experiment. From the analysis of the properties block-based binarization algorithm was discovered that it can be recommended as a commercial halftoning algorithm.
References
2. Ричардсон, Я. Видеокодирование. Н.264 и MPEG-4 — стандарты нового поколения. М.: Техносфера, 2005.
3. Самусев, К.Б., Юшин Г.Н., Рыбин М.В., Лимонов М.Ф. Структурные параметры синтетических опалов: статистический анализ данных электронной микроскопии // Физика твердого тела, 2008, Т. 50, вып. 7. С. 1230–1236.
4. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. on System, Man and Cybernetics, 1979, Vol. SMC-9, № 1.
5. J. Foley, A. van Dam, et al. Computer Graphics: principle and Practice Second Edition, 1990. P. 568–573.
6. Floyd R.W., Steinberg L. An adaptive algorithm for spatial gray-scale // Proceedings Society Information Display, 1976. Vol. 17, № 2. P. 75–78.
7. Горбачев, В.Н., Дроздов В.Н., Яковлева Е.С. Один алгоритм бинаризации полутоновых изображений // Дизайн. Материалы. Технологии. СПб.: СПГУТД, 2009. № 2(9). С. 104–107.
8. Комолова, Н.В., Яковлева Е.С. Adobe Photoshop CS4 для всех. СПб.: БХВ-Петербург, 2009.
9. Lau D.L., Arche G.R. Modern digital halftoning. Second edition. USA: CRC Press, 2008.
10. Wang Z., Bovik A.C. Modern Image Quality Assessment. New York: Morgan and Claypool Publishing Company, 2006.
11. Daniel L. Lau, Robert Ulichney, Gonzalo R. Arce Cam. Fundamental Characteristics of Halftone Textures: Blue-Noise and Green-Noise // Image Systems Laboratory, march 31, 2003.
12. Velho and Gomes J.M. Digital halftoning with space filling curves // Computer Graphics, 1991. Vol. 25, № 4. P. 81—90.
13. Ulichney R.A. Digital Halftoning. MIT Press, Cambridge, MA, 1987.
14. Rosenberg C.J. Measurement-based evaluation of a printer dot model for halftone algorithm tone correction // Journal of Electronic Imaging, 1993. Vol. 2, № 3, P. 205—212.
15. Yao M., Parker K.J. Dot gain compensation in the blue noise mask. In B. E. Rogowitz and J. P. Allebach, editors, Proceedings of SPIE, Human Vision, Visual Processing, and Digital Display VI, 1995. Vol. 2411, P. 221–227.
16. T. N. Pappas and D. L. Neuhoff. Printer models and error diffusion // IEEE Transactions on Image Processing, 1995. № 4(1). P. 66–79.
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