Abstract

Color constancy is the ability to recover a stable perceptual estimate of surface reflectance, regardless of the lighting environment. However, we know little about how observers make judgments of the surface color of glossy objects, particularly in complex lighting environments that introduce complex spatial patterns of chromatic variation across an object’s surface. To address this question, we measured thresholds for reflectance discrimination using computer-rendered stimuli under environmental illumination. In Experiment 1, we found that glossiness and shape had small effects on discrimination thresholds. Importantly, discrimination ellipses extended along the direction in which the chromaticities in the environmental illumination spread. In Experiment 2, we also found that the observers’ abilities to judge surface colors were worse in lighting environments with an atypical chromatic distribution.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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References

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2017 (2)

R. J. Lee and H. E. Smithson, “Motion of glossy objects does not promote separation of lighting and surface colour,” R. Soc. Open Sci. 4, 171290 (2017).
[Crossref]

K. Uchikawa, T. Morimoto, and T. Matsumoto, “Understanding individual differences in color appearance of “#TheDress” based on the optimal color hypothesis,” J. Vis. 17(8):10, 1–14 (2017).
[Crossref]

2016 (3)

A. Radonjic, B. Pearce, S. Aston, A. Krieger, H. Dubin, N. P. Cottaris, D. H. Brainard, and A. C. Hurlbert, “Illumination discrimination in real and simulated scenes,” J. Vis. 16(11):2, 1–18 (2016).
[Crossref]

S. M. C. Nascimento, K. Amano, and D. H. Foster, “Spatial distributions of local illumination color in natural scenes,” Vis. Res. 120, 39–44 (2016).
[Crossref]

R. J. Lee and H. E. Smithson, “Low levels of specularity support operational color constancy, particularly when surface and illumination geometry can be inferred,” J. Opt. Soc. Am. A 33, A306–A318 (2016).
[Crossref]

2015 (6)

A. Radonjić, N. P. Cottaris, and D. H. Brainard, “Color constancy supports cross-illumination color selection,” J. Vis. 15(6), 13 (2015).
[Crossref]

A. Radonjić, N. P. Cottaris, and D. H. Brainard, “Color constancy in a naturalistic goal-directed task,” J. Vis. 15(13), 3 (2015).
[Crossref]

A. C. Chadwick and R. W. Kentridge, “The perception of gloss: a review,” Vis. Res. 109, 221–235 (2015).
[Crossref]

L. E. Welbourne, A. B. Morland, and A. R. Wade, “Human colour perception changes between seasons,” Curr. Biol. 25, R646–R647 (2015).
[Crossref]

J. M. Bosten, R. D. Beer, and D. I. A. MacLeod, “What is white?” J. Vis. 15(16):5, 1–19 (2015).
[Crossref]

M. T. Karl, R. Gegenfurtner, and M. Bloj, “The many colours of ‘the dress’,” Curr. Biol. 25, R523–R548 (2015).
[Crossref]

2014 (5)

B. S. Heasly, N. P. Cottaris, D. P. Lichtman, B. Xiao, and D. H. Brainard, “RenderToolbox3: MATLAB tools that facilitate physically based stimulus rendering for vision research,” J. Vis. 14(2):6 1–22 (2014).
[Crossref]

B. Pearce, S. Crichton, M. Mackiewicz, G. D. Finlayson, and A. Hurlbert, “Chromatic illumination discrimination ability reveals that human colour constancy is optimised for blue daylight illuminations,” PLoS ONE 9, e87989 (2014).
[Crossref]

Y. Morgenstern, W. S. Geisler, and R. F. Murray, “Human vision is attuned to the diffuseness of natural light,” J. Vis. 14(9):15, 1–18 (2014).
[Crossref]

R. W. Fleming, “Visual perception of materials and their properties,” Vis. Res. 94, 62–75 (2014).
[Crossref]

J. Granzier, R. Vergne, and K. Gegenfurtner, “The effects of surface gloss and roughness on color constancy for real 3-D objects,” J. Vis. 14(2):16, 1–20 (2014).
[Crossref]

2012 (5)

B. Xiao, B. Hurst, L. MacIntyre, and D. H. Brainard, “The color constancy of three-dimensional objects,” J. Vis. 12(4), 6 (2012).
[Crossref]

R. J. Lee and H. E. Smithson, “Context-dependent judgments of color that might allow color constancy in scenes with multiple regions of illumination,” J. Opt. Soc. Am. A 29, A247–A257 (2012).
[Crossref]

P. J. Marlow, J. Kim, and B. L. Anderson, “The perception and misperception of specular surface reflectance,” Curr. Biol. 22, 1909–1913 (2012).
[Crossref]

I. Motoyoshi and H. Matoba, “Variability in constancy of the perceived surface reflectance across different illumination statistics,” Vis. Res. 53, 30–39 (2012).
[Crossref]

K. Uchikawa, K. Fukuda, Y. Kitazawa, and D. I. A. MacLeod, “Estimating illuminant color based on luminance balance of surfaces,” J. Opt. Soc. Am. A 29, A133–A143 (2012).
[Crossref]

2011 (3)

M. A. Webster, “Adaptation and visual coding,” J. Vis. 11(5):3, 1–23 (2011).
[Crossref]

J. Kim, P. Marlow, and B. L. Anderson, “The perception of gloss depends on highlight congruence with surface shading,” J. Vis. 11(9), 4 (2011).
[Crossref]

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

2010 (7)

K. Doerschner, “Estimating the glossiness transfer function induced by illumination change and testing its transitivity,” J. Vis. 10(4):8, 1–9 (2010).
[Crossref]

K. Doerschner, L. T. Maloney, and H. Boyaci, “Perceived glossiness in high dynamic range scenes,” J. Vis. 10(9), 11 (2010).
[Crossref]

L. T. Maloney and D. H. Brainard, “Color and material perception: achievements and challenges,” J. Vis. 10(9):19 (2010).
[Crossref]

G. Wendt, F. Faul, V. Ekroll, and R. Mausfeld, “Disparity, motion, and color information improve gloss constancy performance,” J. Vis. 10(9), 7 (2010).
[Crossref]

F. Leloup, M. R. Pointer, J. De Brabanter, P. Dutre, and P. Hanselaer, “The influence of the illumination geometry and luminance contrast on gloss perception,” J. Opt. Soc. Am. A 27, 2046–2054 (2010).
[Crossref]

M. Giesel and K. R. Gegenfurtner, “Color appearance of real objects varying in material, hue, and shape,” J. Vis. 10(9), 10 (2010).
[Crossref]

M. Olkkonen and D. H. Brainard, “Perceived glossiness and lightness under real-world illumination,” J. Vis. 10(9):5, 1–19 (2010).
[Crossref]

2008 (3)

B. Xiao and D. H. Brainard, “Surface gloss and color perception of 3D objects,” Vis. Neurosci. 25, 371–385 (2008).

L. Sharan, Y. Li, I. Motoyoshi, S. Nishida, and E. H. Adelson, “Image statistics for surface reflectance perception,” J. Opt. Soc. Am. A 25, 846–865 (2008).
[Crossref]

T. Hansen, M. Giesel, and K. R. Gegenfurtner, “Chromatic discrimination of natural objects,” J. Vis. 8(1):2, 1–19 (2008).
[Crossref]

2007 (2)

K. Doerschner, H. Boyaci, and L. T. Maloney, “Testing limits on matte surface color perception in three-dimensional scenes with complex light fields,” Vis. Res. 47, 3409–3423 (2007).
[Crossref]

I. Motoyoshi, S. Nishida, L. Sharan, and E. H. Adelson, “Image statistics and the perception of surface qualities,” Nature 447, 206–209 (2007).
[Crossref]

2005 (1)

J. Berzhanskaya, G. Swaminathan, J. Beck, and E. Mingolla, “Remote effects of highlights on gloss perception,” Perception 34, 565–575 (2005).
[Crossref]

2004 (7)

J. T. Todd, J. F. Norman, and E. Mingolla, “Lightness constancy in the presence of specular highlights,” Psychol. Sci. 15, 33–39 (2004).
[Crossref]

R. O. Dror, A. S. Willsky, and E. H. Adelson, “Statistical characterization of real-world illumination,” J. Vis. 4(9), 821–837 (2004).
[Crossref]

H. Boyaci, K. Doerschner, and L. T. Maloney, “Perceived surface color in binocularly viewed scenes with two light sources differing in chromaticity,” J. Vis. 4(9), 664–679 (2004).
[Crossref]

K. Doerschner, H. Boyaci, and L. T. Maloney, “Human observers compensate for secondary illumination originating in nearby chromatic surfaces,” J. Vis. 4(2), 92–105 (2004).
[Crossref]

Y. Ling and A. Hurlbert, “Color and size interactions in a real 3D object similarity task,” J. Vis. 4(9), 721–734 (2004).
[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).
[Crossref]

R. W. Fleming, A. Torralba, and E. H. Adelson, “Specular reflections and the perception of shape,” J. Vis. 4(9), 798–820 (2004).
[Crossref]

2003 (2)

R. W. Fleming, R. O. Dror, and E. H. Adelson, “Real-world illumination and the perception of surface reflectance properties,” J. Vis. 3(5):3, 347–368 (2003).
[Crossref]

J. N. Yang and S. K. Shevell, “Surface color perception under two illuminants: the second illuminant reduces color constancy,” J. Vis. 3(5), 369–379 (2003).
[Crossref]

2002 (3)

J. N. Yang and S. K. Shevell, “Stereo disparity improves color constancy,” Vis. Res. 42, 1979–1989 (2002).
[Crossref]

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

J. Neitz, J. Carroll, Y. Yamauchi, M. Neitz, and D. R. Williams, “Color perception is mediated by a plastic neural mechanism that is adjustable in adults,” Neuron 35, 783–792 (2002).
[Crossref]

2001 (2)

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
[Crossref]

J. N. Yang and L. T. Maloney, “Illuminant cues in surface color perception: tests of three candidate cues,” Vis. Res. 41, 2581–2600 (2001).
[Crossref]

2000 (3)

A. Stockman and L. T. Sharpe, “The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype,” Vis. Res. 40, 1711–1737 (2000).
[Crossref]

N. Justin Marshall, “Communication and camouflage with the same ‘bright’ colours in reef fishes,” Philos. Trans. R. Soc. B 355, 1243–1248 (2000).
[Crossref]

B. Smits, “An RGB to spectrum conversion for reflectances,” J. Graph. Tools 4, 11–22 (2000).

1998 (1)

1997 (1)

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

1994 (2)

L. Chittka, A. Shmida, N. Troje, and R. Menzel, “Ultraviolet as a component of flower reflections, and the colour perception of Hymenoptera,” Vis. Res. 34, 1489–1508 (1994).
[Crossref]

M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

1993 (1)

T. Yeh, J. Pokorny, and V. C. Smith, “Chromatic discrimination with variation in chromaticity and luminance: data and theory,” Vis. Res. 33, 1835–1845 (1993).
[Crossref]

1992 (2)

G. J. Ward, “Measuring and modeling anisotropic reflection,” ACM SIGGRAPH Comput. Graph. 26, 265–272 (1992).

K. R. Gegenfurtner and D. C. Kiper, “Contrast detection in luminance and chromatic noise,” J. Opt. Soc. Am. A 9, 1880–1888 (1992).
[Crossref]

1986 (1)

1979 (1)

Adelson, E. H.

L. Sharan, Y. Li, I. Motoyoshi, S. Nishida, and E. H. Adelson, “Image statistics for surface reflectance perception,” J. Opt. Soc. Am. A 25, 846–865 (2008).
[Crossref]

I. Motoyoshi, S. Nishida, L. Sharan, and E. H. Adelson, “Image statistics and the perception of surface qualities,” Nature 447, 206–209 (2007).
[Crossref]

R. O. Dror, A. S. Willsky, and E. H. Adelson, “Statistical characterization of real-world illumination,” J. Vis. 4(9), 821–837 (2004).
[Crossref]

R. W. Fleming, A. Torralba, and E. H. Adelson, “Specular reflections and the perception of shape,” J. Vis. 4(9), 798–820 (2004).
[Crossref]

R. W. Fleming, R. O. Dror, and E. H. Adelson, “Real-world illumination and the perception of surface reflectance properties,” J. Vis. 3(5):3, 347–368 (2003).
[Crossref]

Amano, K.

S. M. C. Nascimento, K. Amano, and D. H. Foster, “Spatial distributions of local illumination color in natural scenes,” Vis. Res. 120, 39–44 (2016).
[Crossref]

Anderson, B. L.

P. J. Marlow, J. Kim, and B. L. Anderson, “The perception and misperception of specular surface reflectance,” Curr. Biol. 22, 1909–1913 (2012).
[Crossref]

J. Kim, P. Marlow, and B. L. Anderson, “The perception of gloss depends on highlight congruence with surface shading,” J. Vis. 11(9), 4 (2011).
[Crossref]

Arend, L.

Aston, S.

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Figures (8)

Fig. 1.
Fig. 1. Rendering objects in natural lighting environments. (a) Schematic illustration of the rendering process using environmental illumination. The renderer traces light from the environmental illumination to an object with a particular reflectance, and from there to the viewpoint. The subpanel to the bottom right shows the resultant image of the object and the chromatic distribution of pixels in that image. (b) Chromatic distribution of the diffuse component. (c) Chromatic distribution of the specular component. [(a)–(c)] Chromatic distributions are represented in the MB chromaticity diagram [33]. The black plus symbols indicate equal energy white. The magenta cross symbols indicate the mean chromaticity. The black dashed line indicates the black-body locus.
Fig. 2.
Fig. 2. Chromatic properties of the three lighting environments used in the experiments. (a) Environmental Illumination 1 [“Distant Evening Sun (Hallstatt)”], used in Experiments 1 and 2. (b) Environmental Illumination 2 (“Overcast day at Techgate Donaucity”), used in Experiment 1. (c) Chromatically inverted Environmental Illumination 1, used in Experiment 2. [(a)–(c)] The top panel shows a 2D projected image of the 3D environment map. The middle and bottom panels show, respectively, the 3D and 2D color distributions of the environmental illuminations. The magenta cross and black plus symbols indicate, respectively, the mean chromaticity of the distribution and the chromaticity of equal energy white. The black dashed line indicates the black-body locus.
Fig. 3.
Fig. 3. Chromatic properties of the surface spectral reflectance functions used in the experiments. (a) Eight surface reflectances used to measure reflectance discrimination thresholds from the spectrally flat reference reflectance. Each reflectance is independently normalized by its maximum value for the sake of visibility. Note that in the actual experiments they were normalized so that all reflectance functions would produce stimuli of equal luminance when rendered under equal energy white. (b) The colored circles show the chromaticities of the eight reflectances under equal energy white. The plus symbol shows the chromaticity of the flat reflectance under equal energy white. The black dashed line indicates the black-body locus. [(c)–(f)] Effects of environmental illumination on the mean chromaticity of rendered objects for the conditions in Experiment 1. The square and triangle symbols indicate sphere and bumpy conditions, respectively. The semi-transparent symbols are re-plotted from panel (b), for comparison purposes. (c) Matte objects under Environment 1. (d) Matte objects under Environment 2. (e) Glossy objects under Environment 1. (f) Glossy objects under Environment 2.
Fig. 4.
Fig. 4. Schematic illustration of the procedure. (a) After an initial adaptation to chromatic noise, a series of 4AFC trials was presented, until eight interleaved staircases converged. See the main text for details. (b) An example of stimulus presentation on a single trial. Four objects were simultaneously presented for 2 s. The observer’s task was to select one with a different spectral reflectance. All objects in a trial had the same level of specularity (either matte or glossy) and the same 3D shape (either sphere or bumpy). The viewpoint from which the objects were rendered was different for the four objects presented, and so the distractors were not identical to one another.
Fig. 5.
Fig. 5. Results from Experiment 1. (a) Reflectance discrimination thresholds plotted on a reflectance-based plot, where each data point is represented by the chromaticity of the reflectance function at threshold, viewed under equal energy white. (b) Reflectance discrimination thresholds plotted on a mean-chromaticity-based plot, where each data point is represented by the mean chromaticity of the object at threshold. (a), (b) Different rows show data from different observers. Different columns show different environmental illuminations. The black dashed lines indicate the black-body locus. The red dashed lines in panel (a) indicate the axis that exhibits the maximum variation in chromaticity of the environmental illumination. Data are plotted in a scaled MB chromaticity diagram, where equal energy white corresponds to the origin and each axis is independently scaled by chromatic discrimination thresholds along L / ( L + M ) and S / ( L + M ) that were measured prior to the experiment.
Fig. 6.
Fig. 6. Summary of reflectance discrimination performance across conditions of Experiment 1. (a) Mean area of ellipses measured on the reflectance-based plot. (b) Mean area of ellipses measured on the mean-chromaticity-based plot. (c) Mean eccentricity of ellipses measured on the reflectance-based plot. (d) Mean eccentricity of ellipses measured on the mean-chromaticity-based plot. Error bars indicate ± 1 S.E. across all observers.
Fig. 7.
Fig. 7. Results from Experiment 2, with reflectance discrimination thresholds plotted on a reflectance-based plot [analogous to Fig. 5(a)]. (a) Data obtained with stimuli rendered under Environment 1. (b) Data obtained with stimuli rendered under the chromatically inverted version of Environment 1. The axis scaling is the same as in Fig. 5.
Fig. 8.
Fig. 8. Summary of reflectance discrimination performance across conditions of Experiment 2. (a) Mean area of ellipses measured on the reflectance-based plot. (b) Mean area of ellipses measured on the mean-chromaticity-based plot. (c) Mean eccentricity of ellipses measured on the reflectance-based plot. (d) Mean eccentricity of ellipses measured on the mean-chromaticity-based plot. Error bars indicate ± 1 S.E. across all observers.

Tables (1)

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Table 1. Summary of Conditions in Experiment 1 with Example Objects That Have Spectrally Flat Reflectance Functions

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