Gamut Mapping through Perceptually-Based Contrast Reduction

  • Syed Waqas Zamir
  • Javier Vazquez-Corral
  • Marcelo Bertalmío
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)


In this paper we present a spatial gamut mapping algorithm that relies on a perceptually-based variational framework. Our method adapts a well-known image energy functional whose minimization leads to image enhancement and contrast modification. We show how by varying the importance of the contrast term in the image functional we are able to perform gamut reduction. We propose an iterative scheme that allows our algorithm to successfully map the colors from the gamut of the original image to a given destination gamut while preserving the colors’ perception and texture close to the original image. Both subjective and objective evaluation validate the promising results achieved via our proposed framework.


Gamut Mapping (GM) Gamut Mapping Algorithm (GMA) color contrast variational methods 


  1. 1.
    Alsam, A., Farup, I.: Colour Gamut Mapping as a Constrained Variational Problem. In: Proc. 16th Scandinavian Conference on Image Analysis, pp. 109–118 (2009)Google Scholar
  2. 2.
    Alsam, A., Farup, I.: Spatial Colour Gamut Mapping by Orthogonal Projection of Gradients onto Constant Hue Lines. In: Proc. 8th International Symposium on Visual Computing, pp. 556–565 (2012)Google Scholar
  3. 3.
    Bala, R., Dequeiroz, R., Eschbach, R., Wu, W.: Gamut Mapping to Preserve Spatial Luminance Variations. Journal of Imaging Science and Technology, 122–128 (2001)Google Scholar
  4. 4.
    Bertalmío, M., Caselles, V., Provenzi, E.: Issues About Retinex Theory and Contrast Enhancement. International Journal of Computer Vision 83(1), 101–119 (2009)CrossRefGoogle Scholar
  5. 5.
    Bertalmío, M., Caselles, V., Provenzi, E., Rizzi, A.: Perceptual Color Correction Through Variational Techniques. IEEE Transactions on Image Processing 16(4), 1058–1072 (2007)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Bonnier, N., Schmitt, F., Brettel, H., Berche, S.: Evaluation of Spatial Gamut Mapping Algorithms. In: Proc. 14th Color Imaging Conference (2006)Google Scholar
  7. 7.
    Buchsbaum, G.: A Spatial Processor Model for Object Colour Perception. Journal of the Franklin Institute 310(1), 1–26 (1980)CrossRefMathSciNetGoogle Scholar
  8. 8.
    CIE: Guidelines for the evaluation of gamut mapping algorithms. Technical Report (2004)Google Scholar
  9. 9.
    Ebner, F., Fairchild, M.D.: Gamut mapping from below: Finding minimum perceptual distances for colors outside the gamut volume. Color Research and Application, 402–413 (1997)Google Scholar
  10. 10.
    Farup, I., Gatta, C., Rizzi, A.: A multiscale framework for spatial gamut mapping. IEEE Transactions on Image Processing, 2423–2435 (2007)Google Scholar
  11. 11.
    Katoh, N., Ito, M., Ohno, S.: Three-dimensional gamut mapping using various color difference formulae and color spaces. Journal of Electronic Imaging 4(8), 365–379 (1999)CrossRefGoogle Scholar
  12. 12.
    Kimmel, R., Shaked, D., Elad, M., Sobel, I.: Space-Dependent Color Gamut Mapping: A Variational Approach. IEEE Transactions on Image Processing, 796–803 (2005)Google Scholar
  13. 13.
    Land, E.H., McCann, J.J.: Lightness and Retinex Theory. Journal of the Optical Society of America, 1–11 (1971)Google Scholar
  14. 14.
    Lau, C., Heidrich, W., Mantiuk, R.: Cluster-based color space optimizations. In: Proc. IEEE International Conference on Computer Vision, pp. 1172–1179 (2011)Google Scholar
  15. 15.
    Lissner, I., Preiss, J., Urban, P., Lichtenauer, M.S., Zolliker, P.: Image-Difference Prediction: From Grayscale to Color. IEEE Transactions on Image Processing 22(2), 435–446 (2013)CrossRefMathSciNetGoogle Scholar
  16. 16.
    McCann, J.J.: Lessons Learned from Mondrians Applied to Real Images and Color Gamuts. In: Proc. Color Imaging Conference, pp. 1–8 (1999)Google Scholar
  17. 17.
    McCann, J.J.: A Spatial Colour Gamut Calculation to Optimize Colour Appearance. Colour Image Science: Exploiting Digital Media, 213–233 (2002)Google Scholar
  18. 18.
    Meyer, J., Barth, B.: Color Gamut Matching for Hard Copy. In: Proc. SID Digest, pp. 86–89 (1989)Google Scholar
  19. 19.
    Morovič, J.: Color gamut mapping. John Wiley & Sons (2008)Google Scholar
  20. 20.
    Morovič, J., Wang, Y.: A Multi-Resolution, Full-Colour Spatial Gamut Mapping Algorithm. In: Proc. Color Imaging Conference, pp. 282–287 (2003)Google Scholar
  21. 21.
    Murch, G.M., Taylor, J.M.: Color in Computer Graphics: Manipulating and Matching Color. In: Eurographics Seminar: Advances in Computer Graphics V, pp. 41–47 (1989)Google Scholar
  22. 22.
    Stone, M.C., Cowan, W.B., Beatty, J.C.: Color gamut mapping and the printing of digital color images. ACM Transactions on Graphics 7(4), 249–292 (1988)CrossRefGoogle Scholar
  23. 23.
    Zolliker, P., Simon, K.: Retaining Local Image Information in Gamut Mapping Algorithms. IEEE Transactions on Image Processing 16(3), 664–672 (2007)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Syed Waqas Zamir
    • 1
  • Javier Vazquez-Corral
    • 1
  • Marcelo Bertalmío
    • 1
  1. 1.Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain

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