Influence of scene statistics on colour constancy


The light reflected from an object depends not only on the surface properties of this object but also on the illuminant. The same is true for the excitations of the photoreceptors, which serve as the basis for the perceived colour. However, our visual system has the ability to perceive constant surface colours despite changes in illumination1. The average chromaticity of the retinal image of a scene depends on the illumination, and thus might be used by the visual system to estimate the illumination and to modulate the correction that subserves colour constancy2,3,4. But this measure is not sufficient: a reddish scene under white light can produce the same mean stimulation as a neutral scene in red light. Higher order scene statistics—for example, the correlation between redness and luminance within the image—allow these cases to be distinguished. Here we report that the human visual system does exploit such a statistic when estimating the illuminant, and gives it a weight that is statistically appropriate for the natural environment.

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Figure 1: The ambiguity of the mean image chromaticity for estimating the illuminant, and two possibilities for resolving it using a higher order statistic (luminance–redness correlation).
Figure 2: Dependence of centre test spot settings on the luminance–redness correlation within the surround.
Figure 3: Luminance–redness correlation and mean redness of natural scenes under different illuminations.


  1. 1

    Kraft, J. M. & Brainard, D. H. Mechanisms of color constancy under nearly natural viewing. Proc. Natl Acad. Sci. USA 96, 307–312 (1999).

    ADS  CAS  Article  Google Scholar 

  2. 2

    Buchsbaum, G. A. Spatial processor model for object colour perception. J. Franklin Inst. 310, 1–26 (1980).

    Article  Google Scholar 

  3. 3

    Land, E. H. Recent advances in retinex theory. Vis. Res. 26, 7–21 (1986).

    CAS  Article  Google Scholar 

  4. 4

    Gershon, R. & Jepson, A .D. The computation of color constant descriptors in chromatic images. Color Res. Appl. 14, 325–334 (1989).

    Article  Google Scholar 

  5. 5

    Gilchrist, A. L. & Ramachandran, V. Red rooms in white light appear different from white rooms in red light. Invest. Ophthalmol. Vis. Sci. 33, 756 (1992).

    Google Scholar 

  6. 6

    Lee, H.-C. Method for computing the scene–illuminant chromaticity from specular highlights. J. Opt. Soc. Am. A 3, 1694–1699 (1986).

    ADS  CAS  Article  Google Scholar 

  7. 7

    Tominaga, S. & Wandell, B. A. Standard surface-reflectance model and illuminant estimation. J. Opt. Soc. Am. A 6, 576–584 (1989).

    ADS  CAS  Article  Google Scholar 

  8. 8

    D'Zmura, M. & Lennie, P. Mechanisms of color constancy. J. Opt. Soc. Am. A 3, 1662–1672 (1986).

    ADS  CAS  Article  Google Scholar 

  9. 9

    Maloney, T. M. & Yang, J. N. in Colour Perception: From Light to Object (eds Mausfeld, R. & Heyer, D.) (in the press).

  10. 10

    Funt, B. V. & Drew, M. S. Color space analysis of mutual illumination. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1319–1326 (1993).

    Article  Google Scholar 

  11. 11

    Funt, B. V., Drew, M. S. & Ho, J. Color constancy from mutual reflection. Int. J. Comp. Vis. 6, 5–24 (1991).

    Article  Google Scholar 

  12. 12

    Bloj, M., Kersten, D. & Hurlbert, A. C. Perception of three-dimensional shape influences colour perception through mutual illumination. Nature 402, 877–879 (1999).

    ADS  CAS  Article  Google Scholar 

  13. 13

    Bäuml, K.-H. Color appearance: effects of illuminant changes under different surface collections. J. Opt. Soc. Am. A 11, 531–542 (1994).

    ADS  Article  Google Scholar 

  14. 14

    MacLeod, D. I. A., & Golz, J. in Colour Perception: From Light to Object (eds Mausfeld, R. & Heyer, D.) (in the press).

  15. 15

    Mausfeld, R. Color in Color Vision (eds Backhaus, W. G. K., Kliegel, R. & Werner, J. S.) 219–250 (De Gruyter, Berlin, 1998).

  16. 16

    Mausfeld, R. & Andres, J. A reduced variance of receptor codes in chromatic scenes activates a ‘discounting the illuminant’ mechanism. Perception 27 (Suppl.), 42 (1998).

    Google Scholar 

  17. 17

    Mausfeld, R. & Andres, J. Second order statistics of colour codes modulate transformations that effectuate varying degrees of scene invariance and illumination invariance. Perception (in the press).

  18. 18

    Ruderman, D. L., Cronin, T. W. & Chiao, C. C. Statistics of cone responses to natural images: implications for visual coding. J. Opt. Soc. Am. A 15, 2036–2045 (1998).

    ADS  Article  Google Scholar 

  19. 19

    MacLeod, D. I. A. & Boynton, R. M. Chromaticity diagram showing cone excitation by stimuli of equal luminance. J. Opt. Soc. Am. 69, 1183–1186 (1979).

    ADS  CAS  Article  Google Scholar 

  20. 20

    Golz, J. & MacLeod, D. I. A. Influence of scene statistics on color constancy. Invest. Ophthalmol. Vis. Sci. 40 (Suppl.), S749 (1999).

    Google Scholar 

  21. 21

    Eskew, R. T., McLellan, J. S. & Giulianini, F. in Color Vision: from Genes to Perception (eds Gegenfurtner, K. R. & Sharpe, L. T.) 345–368 (Cambridge Univ. Press, New York, 1999).

    Google Scholar 

  22. 22

    Jenness, J. W. & Shevell, S. K. Color appearance with sparse chromatic context. Vis. Res. 35, 797–805 (1995).

    CAS  Article  Google Scholar 

  23. 23

    Brown, R. O. & MacLeod, D. I. A. Color appearance depends on the variance of surround colors. Curr. Biol. 7, 844–849 (1997).

    CAS  Article  Google Scholar 

  24. 24

    Webster, M. A. & Mollon, J. D. Adaptation and the color statistics of natural images. Vis. Res. 37, 3283–3298 (1997).

    CAS  Article  Google Scholar 

  25. 25

    Brainard, D. H. & Freeman, W. T. Bayesian color constancy. J. Opt. Soc. Am. A, 14, 1393–1411 (1997).

    ADS  CAS  Article  Google Scholar 

  26. 26

    D'Zmura, M., Iverson, G. & Singer, B. in Geometric Representations of Perceptual Phenomena (eds Luce, D., D'Zmura, M., Hoffman, D., Iverson, G. & Romney, A.) 187–202 (Lawrence Erlbaum Associates, Mahwah, 1995).

    Google Scholar 

  27. 27

    Forsyth, D. A. in AI and the Eye (ed. Blake, A. & Troscianko, T.) 201–227 (Wiley, Chichester, 1990).

    Google Scholar 

  28. 28

    Forsyth, D. A. A novel algorithm for color constancy. Int. J. Comp. Vis. 5, 5–36 (1990).

    Article  Google Scholar 

  29. 29

    Wyszecki, G., & Stiles, W. S. Color Science: Concepts and Methods, Quantitative Data and Formulae 2nd edn 145–146 (Wiley, New York, 1982).

    Google Scholar 

  30. 30

    Stockman, A., MacLeod, D. I. A. & Johnson, N. E. Spectral sensitivities of the human cones. J. Opt. Soc. Am. A 10, 2491–2521 (1993).

    ADS  CAS  Article  Google Scholar 

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We thank D. L. Ruderman, T. W. Cronin and C. C. Chiao for their spectral data of natural scenes. This work was supported by the National Eye Institute. J. Golz was supported by the German–American Fulbright Commission.

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Correspondence to Jürgen Golz.

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Golz, J., MacLeod, D. Influence of scene statistics on colour constancy. Nature 415, 637–640 (2002).

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