On the Use of Gaze Information and Saliency Maps for Measuring Perceptual Contrast

  • Gabriele Simone
  • Marius Pedersen
  • Jon Yngve Hardeberg
  • Ivar Farup
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


In this paper, we propose and discuss a novel approach for measuring perceived contrast. The proposed method comes from the modification of previous algorithms with a different local measure of contrast and with a parameterized way to recombine local contrast maps and color channels. We propose the idea of recombining the local contrast maps using gaze information, saliency maps and a gaze-attentive fixation finding engine as weighting parameters giving attention to regions that observers stare at, finding them important. Our experimental results show that contrast measures cannot be improved using different weighting maps as contrast is an intrinsic factor and it’s judged by the global impression of the image.


Color Channel Natural Scene Salient Object Local Contrast Visual Saliency 
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.


  1. 1.
    Michelson, A.: Studies in Optics. University of Chicago Press (1927)Google Scholar
  2. 2.
    King-Smith, P.E., Kulikowski, J.J.: Pattern and flicker detection analysed by subthreshold summation. J. Physiol. 249(3), 519–548 (1975)CrossRefGoogle Scholar
  3. 3.
    Burkhardt, D.A., Gottesman, J., Kersten, D., Legge, G.E.: Symmetry and constancy in the perception of negative and positive luminance contrast. J. Opt. Soc. Am. A 1(3), 309 (1984)CrossRefGoogle Scholar
  4. 4.
    Whittle, P.: Increments and decrements: luminance discrimination. Vision Research (26), 1677–1691 (1986)CrossRefGoogle Scholar
  5. 5.
    Tadmor, Y., Tolhurst, D.: Calculating the contrasts that retinal ganglion cells and lgn neurones encounter in natural scenes. Vision Research 40, 3145–3157 (2000)CrossRefGoogle Scholar
  6. 6.
    Rizzi, A., Algeri, T., Medeghini, G., Marini, D.: A proposal for contrast measure in digital images. In: CGIV 2004 – Second European Conference on Color in Graphics, Imaging and Vision (2004)Google Scholar
  7. 7.
    Rizzi, A., Simone, G., Cordone, R.: A modified algorithm for perceived contrast in digital images. In: CGIV 2008 - Fourth European Conference on Color in Graphics, Imaging and Vision, Terrassa, Spain, IS&T, June 2008, pp. 249–252 (2008)Google Scholar
  8. 8.
    Pedersen, M., Rizzi, A., Hardeberg, J.Y., Simone, G.: Evaluation of contrast measures in relation to observers perceived contrast. In: CGIV 2008 - Fourth European Conference on Color in Graphics, Imaging and Vision, Terrassa, Spain, IS&T, June 2008, pp. 253–256 (2008)Google Scholar
  9. 9.
    Simone, G., Pedersen, M., Hardeberg, J.Y., Rizzi, A.: Measuring perceptual contrast in a multilevel framework. In: Rogowitz, B.E., Pappas, T.N. (eds.) Human Vision and Electronic Imaging XIV, vol. 7240. SPIE (January 2009)Google Scholar
  10. 10.
    Babcock, J.S., Pelz, J.B., Fairchild, M.D.: Eye tracking observers during rank order, paired comparison, and graphical rating tasks. In: Image Processing, Image Quality, Image Capture Systems Conference (2003)Google Scholar
  11. 11.
    Bai, J., Nakaguchi, T., Tsumura, N., Miyake, Y.: Evaluation of image corrected by retinex method based on S-CIELAB and gazing information. IEICE trans. on Fundamentals of Electronics, Communications and Computer Sciences E89-A(11), 2955–2961 (2006)CrossRefGoogle Scholar
  12. 12.
    Pedersen, M., Hardeberg, J.Y., Nussbaum, P.: Using gaze information to improve image difference metrics. In: Rogowitz, B., Pappas, T. (eds.) Human Vision and Electronic Imaging VIII (HVEI 2008), San Jose, USA. SPIE proceedings, vol. 6806. SPIE (January 2008)Google Scholar
  13. 13.
    Endo, C., Asada, T., Haneishi, H., Miyake, Y.: Analysis of the eye movements and its applications to image evaluation. In: IS&T and SID’s 2nd Color Imaging Conference: Color Science, Systems and Applications, pp. 153–155 (1994)Google Scholar
  14. 14.
    Mackworth, N.H., Morandi, A.J.: The gaze selects informative details with pictures. Perception & psychophyscics 2, 547–552 (1967)CrossRefGoogle Scholar
  15. 15.
    Underwood, G., Foulsham, T.: Visual saliency and semantic incongruency influence eye movements when inspecting pictures. The Quarterly Journal of Experimental Psychology 59, 1931–1949 (2006)CrossRefGoogle Scholar
  16. 16.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)CrossRefzbMATHGoogle Scholar
  17. 17.
    Sharma, P., Cheikh, F.A., Hardeberg, J.Y.: Saliency map for human gaze prediction in images. In: Sixteenth Color Imaging Conference, Portland, Oregon (November 2008)Google Scholar
  18. 18.
    Rajashekar, U., van der Linde, I., Bovik, A.C., Cormack, L.K.: Gaffe: A gaze-attentive fixation finding engine. IEEE Transactions on Image Processing 17, 564–573 (2008)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Henderson, J.M., Williams, C.C., Castelhano, M.S., Falk, R.J.: Eye movements and picture processing during recognition. Perception & Psychophysics 65, 725–734 (2003)CrossRefGoogle Scholar
  20. 20.
    Engeldrum, P.G.: Psychometric Scaling, a toolkit for imaging systems development. Imcotek Press, Winchester (2000)Google Scholar
  21. 21.
    Ponomarenko, N., Lukin, V., Egiazarian, K., Astola, J., Carli, M., Battisti, F.: Color image database for evaluation of image quality metrics. In: International Workshop on Multimedia Signal Processing, Cairns, Queensland, Australia, October 2008, pp. 403–408 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gabriele Simone
    • 1
  • Marius Pedersen
    • 1
  • Jon Yngve Hardeberg
    • 1
  • Ivar Farup
    • 1
  1. 1.Gjøvik University CollegeGjøvikNorway

Personalised recommendations