CFP last date
01 May 2024
Reseach Article

Joint Color Quantization and Dithering Techniques

by Mohammed Ahmed Hassan
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 7
Year of Publication: 2015
Authors: Mohammed Ahmed Hassan

Mohammed Ahmed Hassan . Joint Color Quantization and Dithering Techniques. Communications on Applied Electronics. 3, 7 ( December 2015), 1-8. DOI=10.5120/cae2015651992

@article{ 10.5120/cae2015651992,
author = { Mohammed Ahmed Hassan },
title = { Joint Color Quantization and Dithering Techniques },
journal = { Communications on Applied Electronics },
issue_date = { December 2015 },
volume = { 3 },
number = { 7 },
month = { December },
year = { 2015 },
issn = { 2394-4714 },
pages = { 1-8 },
numpages = {9},
url = { },
doi = { 10.5120/cae2015651992 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:43:38.895527+05:30
%A Mohammed Ahmed Hassan
%T Joint Color Quantization and Dithering Techniques
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 7
%P 1-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Color quantization process is considered in two stages: the selection of an optimal color palette and the mapping of each pixel of the image to a color from the color palette. Since the color palette is limited, some disturbing degradations such as false contours are visible on delivered color quantized images. A common way to overcome this problem is the use of dithering techniques. In this paper, two methods for color quantization are proposed for the use with color dithering techniques in a way that better results will be obtained after dithering. The results show that the proposed methods, when used with dithering techniques, significantly improve the visual quality of the resulting color quantized images compared to the traditional color quantization algorithms.

  1. L. Akarun, D. Ozdemir, and O. Yalcin. A modified quantization algorithm for dithering of color images. Electronics Letters, 32:1185–1186, 1996.
  2. J. Braquelaire and L. Brun. Comparison and optimization of methods of color image quantization. IEEE Transactions on Image Processing, 6(7):1048–1052, 1997.
  3. A. Dekker. Kohonen neural networks for optimal colour quantization. Network Computation in Neural Systems, 5(3):351–367, 1994.
  4. R. Floyd and L. Steinberg. An adaptive algorithm for spatial grey scale. In Proceedings of the Society of Information Display, volume 17, pages 75–77, 1976.
  5. R. Gentile, J. Allebach, and E. Walowit. Quantization of color images based on uniform color spaces. Journal of Imaging Technology, 16(1):11–21, 1990.
  6. R. Gentile, E. Walowit, and J. Allebach. Quantization and multilevel halftoning of color images for near-original image quality. Journal of the Optical Society of America A, 7(6):1019–1026, 1990.
  7. M. Gervautz and W. Purgathofer. A simple method for color quantization: Octree quantization. In Graphics Gems, pages 287–293. Academic Press Professional, Inc., San Diego, CA, USA, 1990.
  8. P. Heckbert. Color image quantization for frame buffer display. In Proceedings of SIGGRAPH, volume 16, pages 297–307, 1982.
  9. J. Jarvis, C. Judice, and W. Ninke. A survey of techniques for the display of continuous tone pictures on bilevel displays. Computer Graphics and Image Processing, 5(1):13– 40, 1976.
  10. G. Joy and Z. Xiang. Center-cut for color-image quantization. The Visual Computer, 10(1):62–66, 1993.
  11. L. Kaplan. Extended fractal analysis for texture classification and segmentation. IEEE Transactions on Image Process, 8(11):1572–1585, 1999.
  12. Y.W. Lim and S.U. Lee. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23:935–952, 1990.
  13. Y. Linde, A. Buzo, and R. M. Gray. An algorithm for vector quantizer desing. IEEE Transactions on Communications, 28(1):84–95, 1980.
  14. Y. Liu and Y. Li. Image feature extraction and segmentation using fractal dimension. In Proceedings of International Conference on Information, Communications and Signal Processing, pages 975–979, 1997.
  15. S. P. Lloyd. Least squares quantization in pcm. IEEE Transactions on Information Theory, 28:129–137, 1982.
  16. M. Mahy, L. Van Eycken, and A. Oosterlinck. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Journal of Color Research and Application, 19:105–121, 1994.
  17. H. Matsumoto and K. Sasazaki. Color image compression with vector quantization. In Proceedings of IEEE Conference on Soft Computing in Industrial Applications, pages 84–88, 2008.
  18. S. Novianto, Y. Suzuki, and J. Maeda. Near optimum estimation of local fractal dimension for image segmentation. Pattern Recognition Letters, 24(1):365–374, 2003.
  19. M. Orchard and C. Bouman. Color quantization of images. IEEE Transactions on Signal Processing, 39(12):2677– 2690, 1991.
  20. N. Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62–66, 1979.
  21. D. Ozdemir and L. Akarun. Fuzzy algorithms for combined quantization and dithering. IEEE Transactions on Image Processing, 10:923–931, 2001.
  22. A. Pentland. Fractal-based description of natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:661–674, 1984.
  23. A. Pentland. Shading into texture. Artificial Intelligence, 29:147–170, 1986.
  24. H. Potlapalli and R. Luo. Fractal-based classification of natural textures. IEEE Transactions on Industrial Electronics, 45(1):142–150, 1998.
  25. A. Robertson. The cie 1976 color-difference formulae. Color Research and Application, 2:7–11, 1977.
  26. X. Rui, C. Chang, and T. Srikanthan. On the initialization and training methods for kohonen self-organizing feature maps in color image quantization. In Proceedings of the 1st IEEE international workshop on electronic design, test and applications, pages 321–325, 2002.
  27. N. Sarkar and B. Chaudhuri. An efficient approach to estimate fractal dimension of textural images. Pattern Recognition, 25(9):1035–1041, 1992.
  28. N. Sarkar and B. Chaudhuri. An efficient differential boxcounting approach to compute fractal dimension of image. IEEE Transactions on Systems, Man and Cybernetics, 24(1):115–120, 1994.
  29. P. Scheunders and S. De Backer. oint quantization and error diffusion of color images using competitive learning. In International Conference on Image Processing, pages 811– 814, 1997.
  30. P. Stucki. MECCA - A multiple-error correcting computation algorithm for bilevel image hardcopy reproduction. Technical Report RZ1060, IBM Research Laboratory, Zurich, Switzerland, 1981.
  31. M. Swain and D. Ballard. Color indexing. International journal of computer vision, 7(1):11–32, 1991.
  32. E. van den Broek, T. Kok, T. Schouten, and L. Vuurpijl. Human-centered content-based image retrieval. In Proceedings of SPIE, volume 6806, page 54, 2008.
  33. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600– 612, 2004.
  34. X. Wu. Efficient statistical computations for optimal color quantization. In Graphics Gems II, pages 126–133. New York: Academic, James Arvo edition, 1991.
  35. C. Yang andW. Tsai. Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle. Pattern Recognition Letters, 19:205–215, 1998.
  36. G. Zorpette. Fractals: not just another pretty picture. IEEE Spectrum, 25(10):29–31, 1988.
Index Terms

Computer Science
Information Sciences


Color Images fractal dimensions error diffusion combined quantization and error diffusion