Advertisement

Spatial Colour Gamut Mapping by Means of Anisotropic Diffusion

  • Ali Alsam
  • Ivar Farup
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)

Abstract

We present a computationally efficient, artifact-free, spatial colour gamut mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal gamut clipping and an ideal spatial gamut mapping. It exploits anisotropic diffusion to reduce the introduction of halos often appearing in spatially gamut mapped images. It is implemented as an iterative method. At iteration level zero, the result is identical to gamut clipping. The more we iterate the more we approach an optimal, spatial gamut mapping result. Our results show that a low number of iterations, 10–20, is sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. The computational complexity for one iteration is O(N), N being the number of pixels. Results based on a challenging small destination gamut supports our claims that it is indeed efficient.

Keywords

spatial gamut mapping colour reproduction anisotropic diffusion 

References

  1. 1.
    Morovič, J., Ronnier Luo, M.: The fundamentals of gamut mapping: A survey. Journal of Imaging Science and Technology 45(3), 283–290 (2001)Google Scholar
  2. 2.
    Bala, R., de Queiroz, R., Eschbach, R., Wu, W.: Gamut mapping to preserve spatial luminance variations. Journal of Imaging Science and Technology 45(5), 436–443 (2001)Google Scholar
  3. 3.
    Kimmel, R., Shaked, D., Elad, M., Sobel, I.: Space-dependent color gamut mapping: A variational approach. IEEE Trans. Image Proc. 14(6), 796–803 (2005)CrossRefGoogle Scholar
  4. 4.
    Farup, I., Gatta, C., Rizzi, A.: A multiscale framework for spatial gamut mapping. IEEE Trans. Image Process. 16(10) (2007), doi:10.1109/TIP.2007.904946.Google Scholar
  5. 5.
    Giesen, J., Schubert, E., Simon, K., Zolliker, P.: Image-dependent gamut mapping as optimization problem. IEEE Trans. Image Process. 16(10), 2401–2410 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Eschbach, R.: Image reproduction: An oxymoron? Colour: Design & Creativity 3(3), 1–6 (2008)Google Scholar
  7. 7.
    Land, E.H., McCann, J.J.: Lightness and retinex theory. Journal of the Optical Society of America 61(1), 1–11 (1971)CrossRefGoogle Scholar
  8. 8.
    McCann, J.J.: A spatial colour gamut calculation to optimise colour appearance. In: MacDonald, L.W., Luo, M.R. (eds.) Colour Image Science, pp. 213–233. John Wiley & Sons Ltd., Chichester (2002)Google Scholar
  9. 9.
    Meyer, J., Barth, B.: Color gamut matching for hard copy. SID Digest, 86–89 (1989)Google Scholar
  10. 10.
    Morovič, J., Wang, Y.: A multi-resolution, full-colour spatial gamut mapping algorithm. In: Proceedings of IS&T and SID’s 11th Color Imaging Conference: Color Science and Engineering: Systems, Technologies, Applications, Scottsdale, Arizona, pp. 282–287 (2003)Google Scholar
  11. 11.
    Eschbach, R., Bala, R., de Queiroz, R.: Simple spatial processing for color mappings. Journal of Electronic Imaging 13(1), 120–125 (2004)CrossRefGoogle Scholar
  12. 12.
    Zolliker, P., Simon, K.: Retaining local image information in gamut mapping algorithms. IEEE Trans. Image Proc. 16(3), 664–672 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Nakauchi, S., Hatanaka, S., Usui, S.: Color gamut mapping based on a perceptual image difference measure. Color Research and Application 24(4), 280–291 (1999)CrossRefGoogle Scholar
  14. 14.
    Bakke, A.M., Farup, I., Hardeberg, J.Y.: Evaluation of algorithms for the determination of color gamut boundaries. Journal of Imaging Science and Technology 54(5), 050502–050511 (2010)CrossRefGoogle Scholar
  15. 15.
    Balasubramanian, R., Dalal, E.: A method for quantifying the color gamut of an output device. In: Color Imaging: Device-Independent Color, Color Hard Copy, and Graphic Arts II, San Jose, CA. Proc. SPIE, vol. 3018 (January 1997)Google Scholar
  16. 16.
    Alsam, A., Farup, I.: Colour gamut mapping as a constrained variational problem. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 109–118. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Perona, P., Malik, J.: Scale-space and edge detecion using anisotropic diffusion. IEEE Trans. Image Proc. 12(7), 629–639 (1990)Google Scholar
  18. 18.
    Battiato, S., Gallo, G., Stanco, F.: Smart interpolation by anisotropic diffusion. In: International Conference on Image Analysis and Processing, vol. 0, p. 572 (2003)Google Scholar
  19. 19.
    Gali, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., Seide, H.-P.: Image compression with anisotropic diffusion. Journal of Mathematical Imaging and Vision 31(255-269) (2008)Google Scholar
  20. 20.
    Lin, Z., Islam, S.: An adaptive edge-preserving variational framework for color image regularization. In: IEEE International Conference on Image Processing, ICIP 2005, pp. 101–104 (2005)Google Scholar
  21. 21.
    CIE Technical Committee 8-03. Guidelines for the evaluation of gamut mapping algorithms. Technical Report 156, CIE (2003)Google Scholar
  22. 22.
    Dugay, F., Farup, I., Hardeberg, J.Y.: Perceptual evaluation of color gamut mapping algorithms. Color Research and Application 33(6), 470–476 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ali Alsam
    • 1
  • Ivar Farup
    • 2
  1. 1.Sør-Trøndelag University CollegeTrondheimNorway
  2. 2.Gjøvik University CollegeGjøvikNorway

Personalised recommendations