Abstract

We measure the color fidelity of visual scenes that are rendered under different (simulated) illuminants and shown on a calibrated LCD display. Observers make triad illuminant comparisons involving the renderings from two chromatic test illuminants and one achromatic reference illuminant shown simultaneously. Four chromatic test illuminants are used: two along the daylight locus (yellow and blue), and two perpendicular to it (red and green). The observers select the rendering having the best color fidelity, thereby indirectly judging which of the two test illuminants induces the smallest color differences compared to the reference. Both multicolor test scenes and natural scenes are studied. The multicolor scenes are synthesized and represent ellipsoidal distributions in CIELAB chromaticity space having the same mean chromaticity but different chromatic orientations. We show that, for those distributions, color fidelity is best when the vector of the illuminant change (pointing from neutral to chromatic) is parallel to the major axis of the scene’s chromatic distribution. For our selection of natural scenes, which generally have much broader chromatic distributions, we measure a higher color fidelity for the yellow and blue illuminants than for red and green. Scrambled versions of the natural images are also studied to exclude possible semantic effects. We quantitatively predict the average observer response (i.e., the illuminant probability) with four types of models, differing in the extent to which they incorporate information processing by the visual system. Results show different levels of performance for the models, and different levels for the multicolor scenes and the natural scenes. Overall, models based on the scene averaged color difference have the best performance. We discuss how color constancy algorithms may be improved by exploiting knowledge of the chromatic distribution of the visual scene.

© 2013 Optical Society of America

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  1. G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1–26 (1980).
    [CrossRef]
  2. D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5(1), 5–35 (1990).
    [CrossRef]
  3. G. D. Finlayson and G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vis. 42, 127–144 (2001).
    [CrossRef]
  4. L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. A 3, 29–33 (1986).
    [CrossRef]
  5. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997).
    [CrossRef]
  6. B. Funt, K. Barnard, and L. Martin, “Is colour constancy good enough?” Proceedings of the 5th European Conference on Computer Vision (Springer-Verlag, 1998), pp. 445–459.
  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. L. Arend, A. Reeves, J. Schirillo, and R. Goldstein, “Simultaneous color constancy: patterns with diverse Munsell values,” J. Opt. Soc. Am. A 8, 661–672 (1991).
    [CrossRef]
  9. H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Phil. Trans. R. Soc. B 360, 1329–1346 (2005).
    [CrossRef]
  10. D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700 (2011).
    [CrossRef]
  11. O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation for color appearance and discrimination,” Vis. Res. 40, 1813–1826 (2000).
    [CrossRef]
  12. M. P. Lucassen, T. Gevers, and A. Gijsenij, “Color fidelity of chromatic distributions by triad illuminant comparison,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop: Perception and Visual Signal Analysis (2011), pp. 1–6.
  13. “Colorimetry,” , 2nd ed. (Central Bureau of the CIE, 1986) .
  14. “Industrial Colour-Difference Evaluation,” (Central Bureau of the CIE, 1995).
  15. “Improvement to Industrial Colour-Difference Evaluation,” (Central Bureau of the CIE, 2000).
  16. X. Zhang and B. A. Wandell, “A spatial extension of CIELAB for digital color image reproduction,” in Society for Information Display 96 Digest, San Diego, California, 1996, pp. 731–734.
  17. A. Toet and M. P. Lucassen, “A new universal colour image fidelity metric,” Displays 24, 197–207 (2003).
    [CrossRef]
  18. J. Van de Weijer, T. Gevers, and A. Gijsenij, “Edge based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
    [CrossRef]
  19. F. Ciurea and B. Funt, “A large image database for color constancy research,” in IS&T 11th Color Imaging Conference, Scottsdale, Arizona, 2003, pp. 160–164.
  20. C. Van Trigt, “Smoothest reflectance functions. I. Definition and main results,” J. Opt. Soc. Am. A 7, 1891–1904 (1990).
    [CrossRef]
  21. P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vis. 4(2):1, 57–81 (2004).
  22. A. Gijsenij, T. Gevers, and M. P. Lucassen, “A perceptual analysis of distance measures for color constancy,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
    [CrossRef]
  23. M. P. Lucassen, P. Bijl, and J. Roelofsen, “The perception of static colored noise: detection and masking described by CIE94,” Color Res. Appl. 33, 178–191 (2008).
    [CrossRef]
  24. M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis. 7(1), 11–32 (1991).
    [CrossRef]
  25. Bruce Lindbloom, http://www.brucelindbloom.com .
  26. S. A. Ajagamelle, M. Pedersen, and G. Simone, “Analysis of the difference of Gaussians model in image difference metrics,” in IS&T 5th European Conference on Colour in Graphics, Imaging, and Vision, Joensuu, Finland, 2010, pp. 489–496.
  27. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
    [CrossRef]
  28. D. L. Ruderman, T. W. Cronin, and C.-C. Chiao, “Statistics of cone responses to natural images: implications for visual coding,” J. Opt. Soc. Am. A 15, 2036–2045 (1998).
    [CrossRef]
  29. J. Golz and D. I. A. MacLeod, “Influence of scene statistics on colour constancy,” Nature 415, 637–640 (2002).
    [CrossRef]
  30. R. Mausfeld and J. Andres, “Second-order statistics of colour codes modulate transformations that effectuate varying degrees of scene invariance and illumination invariance,” Perception 31, 209–224 (2002).
    [CrossRef]
  31. J. Golz, “The role of chromatic scene statistics in color constancy: spatial integration,” J. Vis. 8(13):6, 1–16 (2008).
    [CrossRef]
  32. J. J. M. Granzier, E. Brenner, and J. B. J. Smeets, “Can illumination estimates provide the basis for color constancy?” J. Vis. 9(3):18, 1–11 (2009).
    [CrossRef]
  33. M. P. Lucassen and J. Walraven, “Quantifying color constancy: evidence for nonlinear processing of cone-specific contrast,” Vis. Res. 33, 739–757 (1993).
    [CrossRef]
  34. D. H. Brainard, “Color constancy in the nearly natural image. 2. Achromatic loci,” J. Opt. Soc. Am. A 15, 307–325 (1998).
    [CrossRef]
  35. D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
    [CrossRef]
  36. T. Hansen, S. Walter, and K. R. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. Vis. 7(4):2, 1–15 (2007).
    [CrossRef]
  37. C. Arnold, “Surface color perception under different illuminants and surface collections,” Unveröffentlichte Dissertation (Universität Regensburg, 2009).
  38. “Colour Rendering (TC 1-33 closing remarks),” (CIE Central Bureau, Vienna, 1999).
  39. M. P. Lucassen, A. Gijsenij, and T. Gevers, “Comparing objective and subjective error measures for color constancy,” in IS&T 4th European Conference on Colour in Graphics, Imaging, and Vision, Terrassa, Spain, 2008.
  40. D. H. Foster, K. Amano, and S. M. C. Nascimento, “Colour constancy from temporal cues: better matches with less variability under fast illuminant changes,” Vis. Res. 41, 285–293 (2001).
    [CrossRef]
  41. N. Serrano, A. Savakis, and J. Luo, “A computationally efficient approach to indoor/outdoor scene classification,” in 16th International Conference on Pattern Recognition (2002), Vol. 4, pp. 146–149.

2011 (1)

D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700 (2011).
[CrossRef]

2009 (2)

A. Gijsenij, T. Gevers, and M. P. Lucassen, “A perceptual analysis of distance measures for color constancy,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

J. J. M. Granzier, E. Brenner, and J. B. J. Smeets, “Can illumination estimates provide the basis for color constancy?” J. Vis. 9(3):18, 1–11 (2009).
[CrossRef]

2008 (2)

J. Golz, “The role of chromatic scene statistics in color constancy: spatial integration,” J. Vis. 8(13):6, 1–16 (2008).
[CrossRef]

M. P. Lucassen, P. Bijl, and J. Roelofsen, “The perception of static colored noise: detection and masking described by CIE94,” Color Res. Appl. 33, 178–191 (2008).
[CrossRef]

2007 (2)

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

T. Hansen, S. Walter, and K. R. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. Vis. 7(4):2, 1–15 (2007).
[CrossRef]

2006 (2)

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

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

2005 (1)

H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Phil. Trans. R. Soc. B 360, 1329–1346 (2005).
[CrossRef]

2004 (1)

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vis. 4(2):1, 57–81 (2004).

2003 (1)

A. Toet and M. P. Lucassen, “A new universal colour image fidelity metric,” Displays 24, 197–207 (2003).
[CrossRef]

2002 (3)

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

J. Golz and D. I. A. MacLeod, “Influence of scene statistics on colour constancy,” Nature 415, 637–640 (2002).
[CrossRef]

R. Mausfeld and J. Andres, “Second-order statistics of colour codes modulate transformations that effectuate varying degrees of scene invariance and illumination invariance,” Perception 31, 209–224 (2002).
[CrossRef]

2001 (2)

D. H. Foster, K. Amano, and S. M. C. Nascimento, “Colour constancy from temporal cues: better matches with less variability under fast illuminant changes,” Vis. Res. 41, 285–293 (2001).
[CrossRef]

G. D. Finlayson and G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vis. 42, 127–144 (2001).
[CrossRef]

2000 (1)

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation for color appearance and discrimination,” Vis. Res. 40, 1813–1826 (2000).
[CrossRef]

1998 (2)

1997 (1)

1993 (1)

M. P. Lucassen and J. Walraven, “Quantifying color constancy: evidence for nonlinear processing of cone-specific contrast,” Vis. Res. 33, 739–757 (1993).
[CrossRef]

1991 (2)

1990 (2)

C. Van Trigt, “Smoothest reflectance functions. I. Definition and main results,” J. Opt. Soc. Am. A 7, 1891–1904 (1990).
[CrossRef]

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5(1), 5–35 (1990).
[CrossRef]

1986 (1)

1980 (1)

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

Ajagamelle, S. A.

S. A. Ajagamelle, M. Pedersen, and G. Simone, “Analysis of the difference of Gaussians model in image difference metrics,” in IS&T 5th European Conference on Colour in Graphics, Imaging, and Vision, Joensuu, Finland, 2010, pp. 489–496.

Amano, K.

D. H. Foster, K. Amano, and S. M. C. Nascimento, “Colour constancy from temporal cues: better matches with less variability under fast illuminant changes,” Vis. Res. 41, 285–293 (2001).
[CrossRef]

Andres, J.

R. Mausfeld and J. Andres, “Second-order statistics of colour codes modulate transformations that effectuate varying degrees of scene invariance and illumination invariance,” Perception 31, 209–224 (2002).
[CrossRef]

Arend, L.

Arnold, C.

C. Arnold, “Surface color perception under different illuminants and surface collections,” Unveröffentlichte Dissertation (Universität Regensburg, 2009).

Ballard, D. H.

M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis. 7(1), 11–32 (1991).
[CrossRef]

Barnard, K.

B. Funt, K. Barnard, and L. Martin, “Is colour constancy good enough?” Proceedings of the 5th European Conference on Computer Vision (Springer-Verlag, 1998), pp. 445–459.

Bijl, P.

M. P. Lucassen, P. Bijl, and J. Roelofsen, “The perception of static colored noise: detection and masking described by CIE94,” Color Res. Appl. 33, 178–191 (2008).
[CrossRef]

Bovik, A. C.

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

Brainard, D. H.

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vis. 4(2):1, 57–81 (2004).

D. H. Brainard, “Color constancy in the nearly natural image. 2. Achromatic loci,” J. Opt. Soc. Am. A 15, 307–325 (1998).
[CrossRef]

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

Brenner, E.

J. J. M. Granzier, E. Brenner, and J. B. J. Smeets, “Can illumination estimates provide the basis for color constancy?” J. Vis. 9(3):18, 1–11 (2009).
[CrossRef]

Buchsbaum, G.

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

Chiao, C.-C.

Ciurea, F.

F. Ciurea and B. Funt, “A large image database for color constancy research,” in IS&T 11th Color Imaging Conference, Scottsdale, Arizona, 2003, pp. 160–164.

Cronin, T. W.

Delahunt, P. B.

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vis. 4(2):1, 57–81 (2004).

Finlayson, G. 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 and G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vis. 42, 127–144 (2001).
[CrossRef]

Forsyth, D. A.

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5(1), 5–35 (1990).
[CrossRef]

Foster, D. H.

D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700 (2011).
[CrossRef]

D. H. Foster, K. Amano, and S. M. C. Nascimento, “Colour constancy from temporal cues: better matches with less variability under fast illuminant changes,” Vis. Res. 41, 285–293 (2001).
[CrossRef]

Freeman, W. T.

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

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

Funt, B.

B. Funt, K. Barnard, and L. Martin, “Is colour constancy good enough?” Proceedings of the 5th European Conference on Computer Vision (Springer-Verlag, 1998), pp. 445–459.

F. Ciurea and B. Funt, “A large image database for color constancy research,” in IS&T 11th Color Imaging Conference, Scottsdale, Arizona, 2003, pp. 160–164.

Gegenfurtner, K. R.

T. Hansen, S. Walter, and K. R. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. Vis. 7(4):2, 1–15 (2007).
[CrossRef]

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation for color appearance and discrimination,” Vis. Res. 40, 1813–1826 (2000).
[CrossRef]

Gevers, T.

A. Gijsenij, T. Gevers, and M. P. Lucassen, “A perceptual analysis of distance measures for color constancy,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

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

M. P. Lucassen, T. Gevers, and A. Gijsenij, “Color fidelity of chromatic distributions by triad illuminant comparison,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop: Perception and Visual Signal Analysis (2011), pp. 1–6.

M. P. Lucassen, A. Gijsenij, and T. Gevers, “Comparing objective and subjective error measures for color constancy,” in IS&T 4th European Conference on Colour in Graphics, Imaging, and Vision, Terrassa, Spain, 2008.

Gijsenij, A.

A. Gijsenij, T. Gevers, and M. P. Lucassen, “A perceptual analysis of distance measures for color constancy,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

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

M. P. Lucassen, T. Gevers, and A. Gijsenij, “Color fidelity of chromatic distributions by triad illuminant comparison,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop: Perception and Visual Signal Analysis (2011), pp. 1–6.

M. P. Lucassen, A. Gijsenij, and T. Gevers, “Comparing objective and subjective error measures for color constancy,” in IS&T 4th European Conference on Colour in Graphics, Imaging, and Vision, Terrassa, Spain, 2008.

Goldstein, R.

Golz, J.

J. Golz, “The role of chromatic scene statistics in color constancy: spatial integration,” J. Vis. 8(13):6, 1–16 (2008).
[CrossRef]

J. Golz and D. I. A. MacLeod, “Influence of scene statistics on colour constancy,” Nature 415, 637–640 (2002).
[CrossRef]

Granzier, J. J. M.

J. J. M. Granzier, E. Brenner, and J. B. J. Smeets, “Can illumination estimates provide the basis for color constancy?” J. Vis. 9(3):18, 1–11 (2009).
[CrossRef]

Hansen, T.

T. Hansen, S. Walter, and K. R. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. Vis. 7(4):2, 1–15 (2007).
[CrossRef]

Hordley, S. D.

Kraft, J. M.

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

Longère, P.

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

Lucassen, M. P.

A. Gijsenij, T. Gevers, and M. P. Lucassen, “A perceptual analysis of distance measures for color constancy,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

M. P. Lucassen, P. Bijl, and J. Roelofsen, “The perception of static colored noise: detection and masking described by CIE94,” Color Res. Appl. 33, 178–191 (2008).
[CrossRef]

A. Toet and M. P. Lucassen, “A new universal colour image fidelity metric,” Displays 24, 197–207 (2003).
[CrossRef]

M. P. Lucassen and J. Walraven, “Quantifying color constancy: evidence for nonlinear processing of cone-specific contrast,” Vis. Res. 33, 739–757 (1993).
[CrossRef]

M. P. Lucassen, T. Gevers, and A. Gijsenij, “Color fidelity of chromatic distributions by triad illuminant comparison,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop: Perception and Visual Signal Analysis (2011), pp. 1–6.

M. P. Lucassen, A. Gijsenij, and T. Gevers, “Comparing objective and subjective error measures for color constancy,” in IS&T 4th European Conference on Colour in Graphics, Imaging, and Vision, Terrassa, Spain, 2008.

Luo, J.

N. Serrano, A. Savakis, and J. Luo, “A computationally efficient approach to indoor/outdoor scene classification,” in 16th International Conference on Pattern Recognition (2002), Vol. 4, pp. 146–149.

MacLeod, D. I. A.

J. Golz and D. I. A. MacLeod, “Influence of scene statistics on colour constancy,” Nature 415, 637–640 (2002).
[CrossRef]

Maloney, L. T.

Martin, L.

B. Funt, K. Barnard, and L. Martin, “Is colour constancy good enough?” Proceedings of the 5th European Conference on Computer Vision (Springer-Verlag, 1998), pp. 445–459.

Mausfeld, R.

R. Mausfeld and J. Andres, “Second-order statistics of colour codes modulate transformations that effectuate varying degrees of scene invariance and illumination invariance,” Perception 31, 209–224 (2002).
[CrossRef]

Nascimento, S. M. C.

D. H. Foster, K. Amano, and S. M. C. Nascimento, “Colour constancy from temporal cues: better matches with less variability under fast illuminant changes,” Vis. Res. 41, 285–293 (2001).
[CrossRef]

Pedersen, M.

S. A. Ajagamelle, M. Pedersen, and G. Simone, “Analysis of the difference of Gaussians model in image difference metrics,” in IS&T 5th European Conference on Colour in Graphics, Imaging, and Vision, Joensuu, Finland, 2010, pp. 489–496.

Reeves, A.

Rinner, O.

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation for color appearance and discrimination,” Vis. Res. 40, 1813–1826 (2000).
[CrossRef]

Roelofsen, J.

M. P. Lucassen, P. Bijl, and J. Roelofsen, “The perception of static colored noise: detection and masking described by CIE94,” Color Res. Appl. 33, 178–191 (2008).
[CrossRef]

Ruderman, D. L.

Savakis, A.

N. Serrano, A. Savakis, and J. Luo, “A computationally efficient approach to indoor/outdoor scene classification,” in 16th International Conference on Pattern Recognition (2002), Vol. 4, pp. 146–149.

Schaefer, G.

G. D. Finlayson and G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vis. 42, 127–144 (2001).
[CrossRef]

Schirillo, J.

Serrano, N.

N. Serrano, A. Savakis, and J. Luo, “A computationally efficient approach to indoor/outdoor scene classification,” in 16th International Conference on Pattern Recognition (2002), Vol. 4, pp. 146–149.

Simone, G.

S. A. Ajagamelle, M. Pedersen, and G. Simone, “Analysis of the difference of Gaussians model in image difference metrics,” in IS&T 5th European Conference on Colour in Graphics, Imaging, and Vision, Joensuu, Finland, 2010, pp. 489–496.

Smeets, J. B. J.

J. J. M. Granzier, E. Brenner, and J. B. J. Smeets, “Can illumination estimates provide the basis for color constancy?” J. Vis. 9(3):18, 1–11 (2009).
[CrossRef]

Smithson, H. E.

H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Phil. Trans. R. Soc. B 360, 1329–1346 (2005).
[CrossRef]

Swain, M. J.

M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis. 7(1), 11–32 (1991).
[CrossRef]

Toet, A.

A. Toet and M. P. Lucassen, “A new universal colour image fidelity metric,” Displays 24, 197–207 (2003).
[CrossRef]

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]

Van Trigt, C.

Walraven, J.

M. P. Lucassen and J. Walraven, “Quantifying color constancy: evidence for nonlinear processing of cone-specific contrast,” Vis. Res. 33, 739–757 (1993).
[CrossRef]

Walter, S.

T. Hansen, S. Walter, and K. R. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. Vis. 7(4):2, 1–15 (2007).
[CrossRef]

Wandell, B. A.

L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. A 3, 29–33 (1986).
[CrossRef]

X. Zhang and B. A. Wandell, “A spatial extension of CIELAB for digital color image reproduction,” in Society for Information Display 96 Digest, San Diego, California, 1996, pp. 731–734.

Wang, Z.

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

Xiao, B.

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

Zhang, X.

X. Zhang and B. A. Wandell, “A spatial extension of CIELAB for digital color image reproduction,” in Society for Information Display 96 Digest, San Diego, California, 1996, pp. 731–734.

Color Res. Appl. (1)

M. P. Lucassen, P. Bijl, and J. Roelofsen, “The perception of static colored noise: detection and masking described by CIE94,” Color Res. Appl. 33, 178–191 (2008).
[CrossRef]

Displays (1)

A. Toet and M. P. Lucassen, “A new universal colour image fidelity metric,” Displays 24, 197–207 (2003).
[CrossRef]

IEEE Signal Process. Lett. (1)

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

IEEE Trans. Image Process. (1)

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

Int. J. Comput. Vis. (3)

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5(1), 5–35 (1990).
[CrossRef]

G. D. Finlayson and G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vis. 42, 127–144 (2001).
[CrossRef]

M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis. 7(1), 11–32 (1991).
[CrossRef]

J. Franklin Inst. (1)

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

J. Opt. Soc. Am. A (8)

J. Vis. (5)

P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vis. 4(2):1, 57–81 (2004).

D. H. Brainard, P. Longère, P. B. Delahunt, W. T. Freeman, J. M. Kraft, and B. Xiao, “Bayesian model of human color constancy,” J. Vis. 6(11):10, 1267–1281 (2006).
[CrossRef]

T. Hansen, S. Walter, and K. R. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. Vis. 7(4):2, 1–15 (2007).
[CrossRef]

J. Golz, “The role of chromatic scene statistics in color constancy: spatial integration,” J. Vis. 8(13):6, 1–16 (2008).
[CrossRef]

J. J. M. Granzier, E. Brenner, and J. B. J. Smeets, “Can illumination estimates provide the basis for color constancy?” J. Vis. 9(3):18, 1–11 (2009).
[CrossRef]

Nature (1)

J. Golz and D. I. A. MacLeod, “Influence of scene statistics on colour constancy,” Nature 415, 637–640 (2002).
[CrossRef]

Perception (1)

R. Mausfeld and J. Andres, “Second-order statistics of colour codes modulate transformations that effectuate varying degrees of scene invariance and illumination invariance,” Perception 31, 209–224 (2002).
[CrossRef]

Phil. Trans. R. Soc. B (1)

H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Phil. Trans. R. Soc. B 360, 1329–1346 (2005).
[CrossRef]

Vis. Res. (4)

D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700 (2011).
[CrossRef]

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation for color appearance and discrimination,” Vis. Res. 40, 1813–1826 (2000).
[CrossRef]

M. P. Lucassen and J. Walraven, “Quantifying color constancy: evidence for nonlinear processing of cone-specific contrast,” Vis. Res. 33, 739–757 (1993).
[CrossRef]

D. H. Foster, K. Amano, and S. M. C. Nascimento, “Colour constancy from temporal cues: better matches with less variability under fast illuminant changes,” Vis. Res. 41, 285–293 (2001).
[CrossRef]

Other (13)

N. Serrano, A. Savakis, and J. Luo, “A computationally efficient approach to indoor/outdoor scene classification,” in 16th International Conference on Pattern Recognition (2002), Vol. 4, pp. 146–149.

C. Arnold, “Surface color perception under different illuminants and surface collections,” Unveröffentlichte Dissertation (Universität Regensburg, 2009).

“Colour Rendering (TC 1-33 closing remarks),” (CIE Central Bureau, Vienna, 1999).

M. P. Lucassen, A. Gijsenij, and T. Gevers, “Comparing objective and subjective error measures for color constancy,” in IS&T 4th European Conference on Colour in Graphics, Imaging, and Vision, Terrassa, Spain, 2008.

Bruce Lindbloom, http://www.brucelindbloom.com .

S. A. Ajagamelle, M. Pedersen, and G. Simone, “Analysis of the difference of Gaussians model in image difference metrics,” in IS&T 5th European Conference on Colour in Graphics, Imaging, and Vision, Joensuu, Finland, 2010, pp. 489–496.

M. P. Lucassen, T. Gevers, and A. Gijsenij, “Color fidelity of chromatic distributions by triad illuminant comparison,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop: Perception and Visual Signal Analysis (2011), pp. 1–6.

“Colorimetry,” , 2nd ed. (Central Bureau of the CIE, 1986) .

“Industrial Colour-Difference Evaluation,” (Central Bureau of the CIE, 1995).

“Improvement to Industrial Colour-Difference Evaluation,” (Central Bureau of the CIE, 2000).

X. Zhang and B. A. Wandell, “A spatial extension of CIELAB for digital color image reproduction,” in Society for Information Display 96 Digest, San Diego, California, 1996, pp. 731–734.

F. Ciurea and B. Funt, “A large image database for color constancy research,” in IS&T 11th Color Imaging Conference, Scottsdale, Arizona, 2003, pp. 160–164.

B. Funt, K. Barnard, and L. Martin, “Is colour constancy good enough?” Proceedings of the 5th European Conference on Computer Vision (Springer-Verlag, 1998), pp. 445–459.

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Figures (11)

Fig. 1.
Fig. 1.

Triad illuminant comparison method involves a test scene rendered under a reference illuminant (R) and two different test illuminants (T1 and T2). The visual differences between the scene rendered under reference and the two test illuminants are denoted by Δ1 and Δ2. Global color fidelity of the test scenes under T1 and T2 is measured by observers indicating which of the two differences (Δ1 or Δ2) appears smaller.

Fig. 2.
Fig. 2.

Screenshot of an experimental trial for Data Set 1. The multicolor test scene on the top row represents a chromatic distribution specified in CIELAB color space. For each color element we derive a spectral reflectance function that is used to simulate the effect of illuminant changes. A greenish and a reddish test illuminant is used in this trial to render the bottom left and right images, respectively. Observers indicate which of these two renderings has the best color fidelity compared to the rendering under neutral reference illumination (in the top row). The size of the background is a 39.6°×30.2° visual angle. Images are 16.6°×16.6° each in Data Set 1, and 6.2°×6.2° in Data Sets 2 and 3. Horizontal and vertical separation is 2.0° and 0.9°, respectively.

Fig. 3.
Fig. 3.

Multicolor stimuli of Data Set 1 under D65 reference illumination (top row) and density plots of their chromatic distributions plotted in the CIELAB a*, b* chromaticity plane (bottom row, a* on the horizontal axis). The numbers above the top images label the distributions described in Table 1. Angle θ denotes the angle between the positive a* axis and the major axis of the distribution in the a*b* plane of CIELAB color space.

Fig. 4.
Fig. 4.

Image Data Set 2, containing 5 natural images per chromatic distribution. Angle Φ denotes the angle in CIELAB a*b* chromaticity space between the axis of the first principal component and the horizontal line parallel to the a* axis. Numbers below the images are labels and correspond to the image numbers in Table 2.

Fig. 5.
Fig. 5.

Density plots of the chromatic distributions in CIELAB a*b* chromaticity space of the natural images shown in Fig. 4. Labels below the images correspond to those in Fig. 4 and the image numbers in Table 2.

Fig. 6.
Fig. 6.

Image Data Set 3. These images were obtained by first pixelating the images of Data Set 2 (8×8 pixel blocks receiving the average color) and then scrambling (spatial relocation). This leaves the global chromatic distribution intact but destroys image semantics.

Fig. 7.
Fig. 7.

Daylight locus and positions of the neutral illuminant and the four chromatic illuminants in CIE 1931 x, y chromaticity space (left) and CIE 1976 a*b* space (right).

Fig. 8.
Fig. 8.

Relative SPD (in arbitrary units, a.u.) of the neutral reference illuminant and the four chromatic illuminants used in the experiments. The illuminants were created with the CIE basis functions for spectral variations in natural daylight, and were modified in purity such that they elicit equal distributions of changes in the reflected light signal (see Fig. 7 also and text for explanation).

Fig. 9.
Fig. 9.

Cumulative distributions of the squared changes in the reflected light signal (ΔL), due to a change from neutral illumination to one of the four chromatic illuminants (indicated in the legend). The change in the reflected light signal is defined in Eq. (4). The figure shows that the distributions are very similar, which was achieved by adjusting the purity and distance of the chromatic illuminants to the neutral point, as described in the text.

Fig. 10.
Fig. 10.

Visual scores per chromatic illuminant (red, green, yellow, blue), averaged across subjects, obtained for the five different chromatic distributions. The plots in the left column show the visual scores for the three image data sets, and the plots in the right column show the same data normalized to the visual scores for the first chromatic distribution. The higher the visual score, the more often the illuminant was (indirectly) judged as having higher color fidelity. Error bars denote ±1 SEM.

Fig. 11.
Fig. 11.

Cumulative distribution of (ΔL)2 for image 1 of Data Set 2 (see Fig. 4 also). The line colors code the illuminant color (blue, yellow, green, red from top to bottom).

Tables (6)

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Table 1. Specification of the Five Chromatic Distributions (900 Samples) under D65 Reference Illumination

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Table 2. Parameters of the Chromatic Distributions of the 25 Images Shown in Fig. 4 (Data Set 2), under D65 Reference Illumination

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Table 3. Parameters of the Chromatic Distributions of the 25 Images Shown in Fig. 6 (Data Set 3), under D65 Reference Illumination

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Table 4. Observed Probability of Test Illuminants R, G, Y, B as Being Selected by the Observers, for Each of the Five Chromatic Distributions and Six Illuminant Combinations of Data Set 1

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Table 5. Overview of Models and Output Variables Used for Predicting the Illuminant Probability with Eqs. (6) and (7)

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Table 6. Model Performances (in %OK) for the Three Data Sets Separately and as Weighted Average

Equations (12)

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

X=kλE(λ)ρ(λ)x¯(λ)dλ,Y=kλE(λ)ρ(λ)y¯(λ)dλ,Z=kλE(λ)ρ(λ)z¯(λ)dλ,
k=100λE(λ)y¯(λ)dλ,
L=λE(λ)ρ(λ)dλ.
ΔL=L2L1=λ[E2(λ)E1(λ)]ρ(λ)dλ.
E(λ)=E(λ)+xED65(λ)1+x,
P=eη1+eη,
η=c(varivarj),
cdf=1ce(dbin),
NHI=j=1nmin(Rj,Tj)j=1nTj,
ΔEavg=1Nj=1NΔEj.
Q=(σxyσxσy)(2x¯y¯x¯2+y¯2)(2σxσyσx2+σy2),
Qcolor=wLQL2+wαQα2+wβQβ2,

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