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On spatial quantization of color images

  • Jens Ketterer
  • Jan Puzicha
  • Marcus Held
  • Martin Fischer
  • Joachim M. Buhmann
  • Dieter Fellner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)

Abstract

Image quantization and dithering are fundamental image processing problems in computer vision and graphics. Both steps are generally performed sequentially and, in most cases, independent of each other. Color quantization with a pixel-wise defined distortion measure and the dithering process with its local neighborhood typically optimize different quality criteria or, frequently, follow a heuristic approach without reference to any quality measure.

In this paper we propose a new model to simultaneously quantize and dither color images. The method is based on a rigorous cost-function approach which optimizes a quality criterion derived from a simplified model of human perception. Optimizations are performed by an efficient multiscale procedure which substantially alleviates the computational burden.

The quality criterion and the optimization algorithms are evaluated on a representative set of artificial and real-world images thereby showing a significant image quality improvement over standard color reduction approaches.

Keywords

Cost Function Color Space Color Quantization Error Diffusion Iterative Conditional Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    P. Heckbert, “Color image quantization for frame buffer displays,” Computer Graphics, vol. 16, no. 3, pp. 297–307, 1982.Google Scholar
  2. 2.
    G. Braudaway, “A procedure for optimum choice of a small number of colors from a large palette for color imaging,” in Electron Imaging'87, 1987.Google Scholar
  3. 3.
    Z. Xiang and G. Joy, “Color image quantization by agglomerative clustering,” IEEE Computer Graphics and Applications, vol. 14, no. 3, pp. 44–48, 1994.CrossRefGoogle Scholar
  4. 4.
    A. Jain and R. Dubes, Algorithms for Clustering Data. Prentice Hall, 1988.Google Scholar
  5. 5.
    R. Ulichney, “Dithering with blue noise,” Proceedings of the IEEE, vol. 76, pp. 56–79, 1988.CrossRefGoogle Scholar
  6. 6.
    R. Floyd and L. Steinberg, “An adaptive algorithm for spatial greyscale,” in Proc. SID, Vol. 17, No. 2, Wiley, 1976.Google Scholar
  7. 7.
    L. Akarůn, D. özdemir, and ö. Yalcun, “Joint quantization and dithering of color images,” in Proceedings of the International Conference on Image Processing (ICIP'96), pp. 557–560, 1996.Google Scholar
  8. 8.
    K. Rose, E. Gurewitz, and G. Fox, “A deterministic annealing approach to clustering,” Pattern Recognition Letters, vol. 11, pp. 589–594, 1990.zbMATHCrossRefGoogle Scholar
  9. 9.
    T. Hofmann, J. Puzicha, and J. Buhmann, “Deterministic annealing for unsupervised texture segmentation,” in Proc. of the EMMCVPR'97, LNCS 1223, pp. 213–228, 1997.Google Scholar
  10. 10.
    F. Heitz, P. Perez, and P. Bouthemy, “Multiscale minimization of global energy functions in some visual recovery problems,” CVGIP: Image Understanding, vol. 59, no. 1, pp. 125–134, 1994.CrossRefGoogle Scholar
  11. 11.
    J. Puzicha and J. Buhmann, “Multiscale annealing for real-time unsupervised texture segmentation,” Tech. Rep. IAI-97-4, Institut für Informatik III (a short version appeared in: Proc. ICCV'98, pp. 267–273), 1997.Google Scholar
  12. 12.
    C. I. de L'Eclairage, “Colorimetry.” CIE Pub. 15.2 2nd ed., 1986.Google Scholar
  13. 13.
    D. Alman, “Industrial color difference evaluation,” Color Res. Appl. 18 137–139, 1993.Google Scholar
  14. 14.
    G. Bilbro, W. Snyder, S. Garnier, and J. Gault, “Mean field annealing: A formalism for constructing GNC-like algorithms,” IEEE Transactions on Neural Networks, vol. 3, no. 1, 1992.Google Scholar
  15. 15.
    T. Hofmann, J. Puzicha, and J. Buhmann, “A deterministic annealing framework for textured image segmentation,” Tech. Rep. IAI-TR-96-2, Institut für Informatik III, 1996.Google Scholar
  16. 16.
    J. Puzicha, M. Held, J. Ketterer, J. Buhmann, and D. Fellner, “On spatial quantization of color images,” Tech. Rep. IAI-TR-98-1, Department for Computer Science III, University Bonn, 1998.Google Scholar
  17. 17.
    M. Gervauz and W. Purgathofer, “A simple method for color quantization: Octree quantization,” in Graphic Gems, pp. 287–293, Academic Press, New York, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jens Ketterer
    • 1
  • Jan Puzicha
    • 1
  • Marcus Held
    • 1
  • Martin Fischer
    • 1
  • Joachim M. Buhmann
    • 1
  • Dieter Fellner
    • 1
  1. 1.Institut für Informatik IIIRheinische Friedrich-Wilhelms-UniversitätBonnGermany

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