Abstract

Color constancy algorithms are often evaluated by using a distance measure that is based on mathematical principles, such as the angular error. However, it is unknown whether these distance measures correlate to human vision. Therefore, the main goal of our paper is to analyze the correlation between several performance measures and the quality, obtained by using psychophysical experiments, of the output images generated by various color constancy algorithms. Subsequent issues that are addressed are the distribution of performance measures, suggesting additional and alternative information that can be provided to summarize the performance over a large set of images, and the perceptual significance of obtained improvements, i.e., the improvement that should be obtained before the difference becomes noticeable to a human observer.

© 2009 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108-128 (1977).
    [CrossRef] [PubMed]
  2. G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1-26 (1980).
    [CrossRef]
  3. G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2004), pp. 37-41.
  4. J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007).
    [CrossRef] [PubMed]
  5. F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2003), pp. 160-164.
  6. K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
    [CrossRef]
  7. S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
    [CrossRef]
  8. D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5-36 (1990).
    [CrossRef]
  9. G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001).
    [CrossRef]
  10. G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006).
    [CrossRef]
  11. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997).
    [CrossRef]
  12. M. D'Zmura, G. Iverson, and B. Singer, “Probabilistic color constancy,” in Geometric Representations of Perceptual Phenomena (Lawrence Erlbaum, 1995), pp. 187-202.
  13. P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
    [CrossRef]
  14. M. Ebner, “Evolving color constancy,” Pattern Recogn. Lett. 27, 1220-1229 (2006).
    [CrossRef]
  15. A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. (to be published), http://www.springerlink.com/content/q598825t7654648n/?p=155b2db7234942feaacfcf6d88a50b2c&pi=0. (September 2009).
  16. Color constancy demonstration (Mathematica), http://cat.cvc.uab.es/~joost/code/ColorConstancy.zip.
  17. J. von Kries, “Die gesichtsempfindungen,” in Handbuch der Physiologie des Menschen (1904), Vol. 3, pp. 109-282.
  18. G. West and M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249-258 (1982).
    [CrossRef] [PubMed]
  19. G. D. Finlayson, M. S. Drew, and B. V. Funt, “Color constancy: generalized diagonal transforms suffice,” J. Opt. Soc. Am. A 11, 3011-3019 (1994).
    [CrossRef]
  20. B. V. Funt and B. C. Lewis, “Diagonal versus affine transformations for color correction,” J. Opt. Soc. Am. A 17, 2108-2112 (2000).
    [CrossRef]
  21. Commission Internationale de L'Eclairage (CIE), “Colorimetry,” CIE Publ. no. 15.2, 2nd ed. (CIE, 1986).
  22. Commission Internationale de L'Eclairage (CIE), “Improvement to industrial colour-difference evaluation,” CIE Publ. no. 142-2001 (CIE, 2001).
  23. M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.
  24. G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, 2000).
  25. J. Slater, Modern Television Systems to HDTV and Beyond (Taylor & Francis, 2004).
  26. L. E. Arend, A. Reeves, J. Schirillo, and R. Goldstein, “Simultaneous color constancy: papers with diverse Munsell values,” J. Opt. Soc. Am. A 8, 661-672 (1991).
    [CrossRef] [PubMed]
  27. E. Brunswik, “Zur Entwicklung der Albedowahrnehmung,” Z. Psychol. 109, 40-115 (1928).
  28. P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vision 4, 57-81 (2004).
    [CrossRef]
  29. D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004).
    [CrossRef]
  30. J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
    [CrossRef]
  31. H. A. David, “Ranking from unbalanced paired-comparison data,” Biometrika 74, 432-436 (1987).
    [CrossRef]
  32. R. L. Alfvin and M. D. Fairchild, “Observer variability in metameric color matches using color reproduction media,” Color Res. Appl. 22, 174-188 (1997).
    [CrossRef]
  33. E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
    [CrossRef]
  34. Bruce Lindbloom's web site, http://www.brucelindbloom.com.
  35. R. V. Hogg and E. A. Tanis, Probability and Statistical Inference (Prentice Hall, 2001).
  36. J. W. Tukey, Exploratory Data Analysis (Addison-Wesley, 1977).
  37. H. F. Weisberg, Central Tendency and Variability (Sage Publications, 1992).
  38. A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.
    [CrossRef]
  39. J. van de Weijer, C. Schmid, and J. J. Verbeek, “Using high-level visual information for color constancy,” in IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.
    [CrossRef]
  40. S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
    [CrossRef]
  41. S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
    [CrossRef] [PubMed]
  42. A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy beyond bags of pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
    [CrossRef]
  43. B. V. Funt, K. Barnard, and L. Martin, “Is machine colour constancy good enough?” in Computer Vision--ECCV'98: 5th European Conference on Computer Vision (Springer, 1998), pp. 445-459.
  44. G. D. Finlayson, S. D. Hordley, and P. Morovic, “Colour constancy using the chromagenic constraint,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1079-1086.
  45. C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
    [CrossRef]
  46. S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303-314 (2006).
    [CrossRef]
  47. E. H. Weber, “Der Tastinn und das Gemeingfühl,” in Handwörterbüch der Physiologie (1846), Vol. 3, pp. 481-588.
  48. T. N. Cornsweet, Visual Perception (Academic, 1970).

2008

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
[CrossRef] [PubMed]

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

2007

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007).
[CrossRef] [PubMed]

2006

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006).
[CrossRef]

M. Ebner, “Evolving color constancy,” Pattern Recogn. Lett. 27, 1220-1229 (2006).
[CrossRef]

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303-314 (2006).
[CrossRef]

2004

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vision 4, 57-81 (2004).
[CrossRef]

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004).
[CrossRef]

J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
[CrossRef]

2002

K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
[CrossRef]

2001

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001).
[CrossRef]

2000

1997

R. L. Alfvin and M. D. Fairchild, “Observer variability in metameric color matches using color reproduction media,” Color Res. Appl. 22, 174-188 (1997).
[CrossRef]

D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997).
[CrossRef]

1994

1991

1990

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5-36 (1990).
[CrossRef]

1987

H. A. David, “Ranking from unbalanced paired-comparison data,” Biometrika 74, 432-436 (1987).
[CrossRef]

1982

G. West and M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249-258 (1982).
[CrossRef] [PubMed]

1980

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1-26 (1980).
[CrossRef]

1977

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108-128 (1977).
[CrossRef] [PubMed]

1928

E. Brunswik, “Zur Entwicklung der Albedowahrnehmung,” Z. Psychol. 109, 40-115 (1928).

Alfvin, R. L.

R. L. Alfvin and M. D. Fairchild, “Observer variability in metameric color matches using color reproduction media,” Color Res. Appl. 22, 174-188 (1997).
[CrossRef]

Amano, K.

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004).
[CrossRef]

Anderson, M.

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.

Arend, L. E.

Bailey, J. E.

J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
[CrossRef]

Barnard, K.

K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
[CrossRef]

B. V. Funt, K. Barnard, and L. Martin, “Is machine colour constancy good enough?” in Computer Vision--ECCV'98: 5th European Conference on Computer Vision (Springer, 1998), pp. 445-459.

Bianco, S.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
[CrossRef] [PubMed]

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

Blake, A.

P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Brainard, D. H.

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vision 4, 57-81 (2004).
[CrossRef]

D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997).
[CrossRef]

Brill, M. H.

G. West and M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249-258 (1982).
[CrossRef] [PubMed]

Brunswik, E.

E. Brunswik, “Zur Entwicklung der Albedowahrnehmung,” Z. Psychol. 109, 40-115 (1928).

Buchsbaum, G.

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1-26 (1980).
[CrossRef]

Chakrabarti, A.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy beyond bags of pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Chandrasekar, S.

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.

Ciocca, G.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
[CrossRef] [PubMed]

Ciurea, F.

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2003), pp. 160-164.

Coath, A.

K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
[CrossRef]

Cornsweet, T. N.

T. N. Cornsweet, Visual Perception (Academic, 1970).

Cusano, C.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
[CrossRef] [PubMed]

David, H. A.

H. A. David, “Ranking from unbalanced paired-comparison data,” Biometrika 74, 432-436 (1987).
[CrossRef]

Delahunt, P. B.

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vision 4, 57-81 (2004).
[CrossRef]

Drew, M. S.

D'Zmura, M.

M. D'Zmura, G. Iverson, and B. Singer, “Probabilistic color constancy,” in Geometric Representations of Perceptual Phenomena (Lawrence Erlbaum, 1995), pp. 187-202.

Ebner, M.

M. Ebner, “Evolving color constancy,” Pattern Recogn. Lett. 27, 1220-1229 (2006).
[CrossRef]

Fairchild, M. D.

R. L. Alfvin and M. D. Fairchild, “Observer variability in metameric color matches using color reproduction media,” Color Res. Appl. 22, 174-188 (1997).
[CrossRef]

Finlayson, G. D.

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001).
[CrossRef]

G. D. Finlayson, M. S. Drew, and B. V. Funt, “Color constancy: generalized diagonal transforms suffice,” J. Opt. Soc. Am. A 11, 3011-3019 (1994).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Colour constancy using the chromagenic constraint,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1079-1086.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2004), pp. 37-41.

Forsyth, D. A.

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5-36 (1990).
[CrossRef]

Foster, D. H.

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004).
[CrossRef]

Fredembach, C.

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

Freeman, W. T.

Funt, B. V.

K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
[CrossRef]

B. V. Funt and B. C. Lewis, “Diagonal versus affine transformations for color correction,” J. Opt. Soc. Am. A 17, 2108-2112 (2000).
[CrossRef]

G. D. Finlayson, M. S. Drew, and B. V. Funt, “Color constancy: generalized diagonal transforms suffice,” J. Opt. Soc. Am. A 11, 3011-3019 (1994).
[CrossRef]

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2003), pp. 160-164.

B. V. Funt, K. Barnard, and L. Martin, “Is machine colour constancy good enough?” in Computer Vision--ECCV'98: 5th European Conference on Computer Vision (Springer, 1998), pp. 445-459.

Gasparini, F.

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

Gehler, P. V.

P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Gevers, T.

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007).
[CrossRef] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. (to be published), http://www.springerlink.com/content/q598825t7654648n/?p=155b2db7234942feaacfcf6d88a50b2c&pi=0. (September 2009).

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.
[CrossRef]

Gijsenij, A.

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007).
[CrossRef] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. (to be published), http://www.springerlink.com/content/q598825t7654648n/?p=155b2db7234942feaacfcf6d88a50b2c&pi=0. (September 2009).

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.
[CrossRef]

Goldstein, R.

Gottenbos, R.

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

Hirakawa, K.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy beyond bags of pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Hogg, R. V.

R. V. Hogg and E. A. Tanis, Probability and Statistical Inference (Prentice Hall, 2001).

Hordley, S. D.

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006).
[CrossRef]

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303-314 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Colour constancy using the chromagenic constraint,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1079-1086.

Hubel, P. M.

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001).
[CrossRef]

Iverson, G.

M. D'Zmura, G. Iverson, and B. Singer, “Probabilistic color constancy,” in Geometric Representations of Perceptual Phenomena (Lawrence Erlbaum, 1995), pp. 187-202.

Kirchner, E.

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

Land, E. H.

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108-128 (1977).
[CrossRef] [PubMed]

Lewis, B. C.

Martin, L.

K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
[CrossRef]

B. V. Funt, K. Barnard, and L. Martin, “Is machine colour constancy good enough?” in Computer Vision--ECCV'98: 5th European Conference on Computer Vision (Springer, 1998), pp. 445-459.

Minka, T. P.

P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Morovic, P.

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Colour constancy using the chromagenic constraint,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1079-1086.

Motta, R.

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.

Nascimento, S. M. C.

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004).
[CrossRef]

Neitz, J.

J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
[CrossRef]

Neitz, M.

J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
[CrossRef]

Njo, L.

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

Reeves, A.

Rother, C.

P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Schettini, R.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
[CrossRef] [PubMed]

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

Schirillo, J.

Schmid, C.

J. van de Weijer, C. Schmid, and J. J. Verbeek, “Using high-level visual information for color constancy,” in IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.
[CrossRef]

Sharp, T.

P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Singer, B.

M. D'Zmura, G. Iverson, and B. Singer, “Probabilistic color constancy,” in Geometric Representations of Perceptual Phenomena (Lawrence Erlbaum, 1995), pp. 187-202.

Slater, J.

J. Slater, Modern Television Systems to HDTV and Beyond (Taylor & Francis, 2004).

Stiles, W. S.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, 2000).

Stokes, M.

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.

Supèr, R.

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

Tait, D.

J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
[CrossRef]

Tanis, E. A.

R. V. Hogg and E. A. Tanis, Probability and Statistical Inference (Prentice Hall, 2001).

Tastl, I.

G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006).
[CrossRef]

Trezzi, E.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2004), pp. 37-41.

Tukey, J. W.

J. W. Tukey, Exploratory Data Analysis (Addison-Wesley, 1977).

van de Weijer, J.

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007).
[CrossRef] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. (to be published), http://www.springerlink.com/content/q598825t7654648n/?p=155b2db7234942feaacfcf6d88a50b2c&pi=0. (September 2009).

J. van de Weijer, C. Schmid, and J. J. Verbeek, “Using high-level visual information for color constancy,” in IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.
[CrossRef]

van den Kieboom, G. J.

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

Verbeek, J. J.

J. van de Weijer, C. Schmid, and J. J. Verbeek, “Using high-level visual information for color constancy,” in IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.
[CrossRef]

von Kries, J.

J. von Kries, “Die gesichtsempfindungen,” in Handbuch der Physiologie des Menschen (1904), Vol. 3, pp. 109-282.

Weber, E. H.

E. H. Weber, “Der Tastinn und das Gemeingfühl,” in Handwörterbüch der Physiologie (1846), Vol. 3, pp. 481-588.

Weisberg, H. F.

H. F. Weisberg, Central Tendency and Variability (Sage Publications, 1992).

West, G.

G. West and M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249-258 (1982).
[CrossRef] [PubMed]

Wyszecki, G.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, 2000).

Zickler, T.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy beyond bags of pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

Biometrika

H. A. David, “Ranking from unbalanced paired-comparison data,” Biometrika 74, 432-436 (1987).
[CrossRef]

Color Res. Appl.

R. L. Alfvin and M. D. Fairchild, “Observer variability in metameric color matches using color reproduction media,” Color Res. Appl. 22, 174-188 (1997).
[CrossRef]

E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007).
[CrossRef]

K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002).
[CrossRef]

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303-314 (2006).
[CrossRef]

IEEE Trans. Image Process.

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007).
[CrossRef] [PubMed]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell.

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001).
[CrossRef]

Int. J. Comput. Vis.

G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006).
[CrossRef]

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5-36 (1990).
[CrossRef]

J. Electron. Imaging

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

J. Franklin Inst.

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1-26 (1980).
[CrossRef]

J. Imaging Sci. Technol.

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

J. Math. Biol.

G. West and M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249-258 (1982).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A

J. Vision

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vision 4, 57-81 (2004).
[CrossRef]

Pattern Recogn. Lett.

M. Ebner, “Evolving color constancy,” Pattern Recogn. Lett. 27, 1220-1229 (2006).
[CrossRef]

Sci. Am.

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108-128 (1977).
[CrossRef] [PubMed]

Visual Neurosci.

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004).
[CrossRef]

J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004).
[CrossRef]

Z. Psychol.

E. Brunswik, “Zur Entwicklung der Albedowahrnehmung,” Z. Psychol. 109, 40-115 (1928).

Other

A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. (to be published), http://www.springerlink.com/content/q598825t7654648n/?p=155b2db7234942feaacfcf6d88a50b2c&pi=0. (September 2009).

Color constancy demonstration (Mathematica), http://cat.cvc.uab.es/~joost/code/ColorConstancy.zip.

J. von Kries, “Die gesichtsempfindungen,” in Handbuch der Physiologie des Menschen (1904), Vol. 3, pp. 109-282.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2004), pp. 37-41.

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2003), pp. 160-164.

M. D'Zmura, G. Iverson, and B. Singer, “Probabilistic color constancy,” in Geometric Representations of Perceptual Phenomena (Lawrence Erlbaum, 1995), pp. 187-202.

P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy beyond bags of pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8.
[CrossRef]

B. V. Funt, K. Barnard, and L. Martin, “Is machine colour constancy good enough?” in Computer Vision--ECCV'98: 5th European Conference on Computer Vision (Springer, 1998), pp. 445-459.

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Colour constancy using the chromagenic constraint,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1079-1086.

Bruce Lindbloom's web site, http://www.brucelindbloom.com.

R. V. Hogg and E. A. Tanis, Probability and Statistical Inference (Prentice Hall, 2001).

J. W. Tukey, Exploratory Data Analysis (Addison-Wesley, 1977).

H. F. Weisberg, Central Tendency and Variability (Sage Publications, 1992).

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8.
[CrossRef]

J. van de Weijer, C. Schmid, and J. J. Verbeek, “Using high-level visual information for color constancy,” in IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8.
[CrossRef]

E. H. Weber, “Der Tastinn und das Gemeingfühl,” in Handwörterbüch der Physiologie (1846), Vol. 3, pp. 481-588.

T. N. Cornsweet, Visual Perception (Academic, 1970).

Commission Internationale de L'Eclairage (CIE), “Colorimetry,” CIE Publ. no. 15.2, 2nd ed. (CIE, 1986).

Commission Internationale de L'Eclairage (CIE), “Improvement to industrial colour-difference evaluation,” CIE Publ. no. 142-2001 (CIE, 2001).

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, 2000).

J. Slater, Modern Television Systems to HDTV and Beyond (Taylor & Francis, 2004).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (8)

Fig. 1
Fig. 1

Four examples of the hyperspectral scenes used in this study are shown in (a)–(d), rendered under the neutral D 65 illuminant. In (e)–(h), four examples of the RGB images are shown.

Fig. 2
Fig. 2

Relative spectral power distribution of the illuminants used in the experiments. Left, illuminant spectra; right, illuminants applied to scene 3. The illuminants are created with the CIE basis functions for spectral variations in natural daylight and were scaled such that a perfectly white reflector would have a luminance of 40 cd m 2 . The four chromatic illuminants red, green, yellow, and blue are perceptually at an equal distance (28 Δ E ab ) from the neutral (D65) illuminant.

Fig. 3
Fig. 3

Screen capture of an experimental trial. Subjects indicate which of the two bottom images (resulting from two different color constancy algorithms) is the best match to the upper reference image. Background dimensions are 39.6° × 30.2° visual angle. Horizontal and vertical separation between the images was 2.1° and 1.4°, respectively. The hyperspectral images are 16.6° × 12.7°, the RGB images are 6.2° × 6.2°.

Fig. 4
Fig. 4

Plot of the correlation coefficients of the weighted Euclidean distance with respect to the human observer (psychophysical data). Only the dependency on weight coefficients w R and w G are shown here; w B follows from w B = 1 w R w G . Left, the results of experiments using the hyperspectral data are demonstrated; right, results of the experiments with the RGB images.

Fig. 5
Fig. 5

Distribution of estimated illuminant errors for the White-Patch algorithm, obtained for a set of over 11,000 images.

Fig. 6
Fig. 6

Box plots of the angular error and the perceptual Euclidean distance for several color constancy methods of the framework from [4].

Fig. 7
Fig. 7

Box plots of the angular error and the perceptual Euclidean distance for several gamut-mapping methods taken from [15].

Fig. 8
Fig. 8

Indication of the just noticeable difference with respect to the absolute error level.

Tables (4)

Tables Icon

Table 1 Correlation Coefficients ρ for Several Distance Measures and Color Spaces with Respect to the Subjective Measure a

Tables Icon

Table 2 Ranking of Methods Created by Using Color Constancy Framework of [4]

Tables Icon

Table 3 Ranking of Several Gamut Mapping Methods, from [15]

Tables Icon

Table 4 Relative Differences between the Best-Performing Algorithm and the Other Methods a

Equations (16)

Equations on this page are rendered with MathJax. Learn more.

f ( x ) = ω e ( λ ) c ( λ ) s ( x , λ ) d λ ,
e = ω e ( λ ) c ( λ ) d λ ,
L p = ( f p ( x ) d x d x ) 1 p = k e .
( | n f σ ( x ) x n | p d x ) 1 p = k e n , p , σ ,
f c = D u , c f u ( R c G c B c ) = ( α 0 0 0 β 0 0 0 γ ) ( R u G u B u ) ,
r = R R + G + B , g = G R + G + B , b = B R + G + B .
d euc ( e e , e u ) = ( r e r u ) 2 + ( g e g u ) 2 + ( b e b u ) 2 .
d angle ( e e , e u ) = cos 1 ( e e e u e e e u ) ,
d Mink ( e e , e u ) = ( | r e r u | p + | g e g u | p + | b e b u | p ) 1 p ,
( X Y Z ) = ( 0.4125 0.3576 0.1804 0.2127 0.7152 0.0722 0.0193 0.1192 0.9502 ) ( R G B ) .
C a b * = ( a * ) 2 + ( b * ) 2 , h a b = tan 1 ( b * a * ) ,
PED ( e e , e u ) = w R ( r e r u ) 2 + w G ( g e g u ) 2 + w B ( b e b u ) 2 ,
CCI = b a ,
d gamut ( e e , e u ) = vol ( G e G u ) vol ( G u ) ,
TM = 0.25 Q 1 + 0.5 Q 2 + 0.25 Q 3 .
JND angular = 0.06 × ϵ max ,

Metrics