Abstract

A recently introduced computational theory of visual surface representation, termed gamut relativity, overturns the classical assumption that brightness, lightness, and transparency constitute perceptual dimensions corresponding to the physical dimensions of luminance, diffuse reflectance, and transmittance, respectively. Here I extend the theory to show how surface gloss and lightness can be understood in a unified manner in terms of the vector computation of “layered representations” of surface and illumination properties, rather than as perceptual dimensions corresponding to diffuse and specular reflectance, respectively. The theory simulates the effects of image histogram skewness on surface gloss/lightness and lightness constancy as a function of specular highlight intensity. More generally, gamut relativity clarifies, unifies, and generalizes a wide body of previous theoretical and experimental work aimed at understanding how the visual system parses the retinal image into layered representations of surface and illumination properties.

© 2013 Optical Society of America

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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  56. T. Vladusich, “Brightness scaling according to gamut relativity,” Color. Res. Appl., to be published.
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]

2013

T. Vladusich, “Gamut relativity: a new computational approach to brightness and lightness perception,” J. Vis. 13(1):14, 1–21 (2013).
[CrossRef]

T. Vladusich, “A re-interpretation of transparency perception in terms of gamut relativity,” J. Opt. Soc. Am. A 30, 418–426 (2013).
[CrossRef]

2012

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

F. Faul and V. Ekroll, “Transparent layer constancy,” J. Vis. 12(12):7, 1–26 (2012).
[CrossRef]

R. W. Fleming, “Human perception: visual heuristics in the perception of glossiness,” Curr. Biol. 22, R865–R866 (2012).
[CrossRef]

T. Vladusich, “Simultaneous contrast and gamut relativity in achromatic color perception,” Vis. Res. 69, 49–63 (2012).
[CrossRef]

J. Kim, P. J. Marlow, and B. L. Anderson, “The dark side of gloss,” Nat. Neurosci. 15, 1590–1595 (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]

V. Ekroll and F. Faul, “Basic characteristics of simultaneous color contrast revisited,” Psychol. Sci. 23, 1246–1255 (2012).
[CrossRef]

V. Ekroll and F. Faul, “New laws of simultaneous contrast?” Seeing Perceiving 25, 107–141 (2012).
[CrossRef]

2011

P. Marlow, J. Kim, and B. L. Anderson, “The role of brightness and orientation congruence in the perception of surface gloss,” J. Vis. 11(9):16, 1–12 (2011).
[CrossRef]

B. L. Anderson, “Visual perception of materials and surfaces,” Curr. Biol. 21, R978–R983 (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, 1–19 (2011).
[CrossRef]

F. Faul and V. Ekroll, “On the filter approach to perceptual transparency,” J. Vis. 11(7):7, 1–33 (2011).
[CrossRef]

A. D. Logvinenko and R. Tokunaga, “Lightness constancy and illumination discounting,” Atten. Percept. Psychophys. 73, 1886–1902 (2011).

F. B. Leloup, M. R. Pointer, P. Dutré, and P. Hanselaer, “Luminance-based specular gloss characterization,” J. Opt. Soc. Am. A 28, 1322–1330 (2011).
[CrossRef]

R. W. Fleming, F. Jäkel, and L. T. Maloney, “Visual perception of thick transparent materials,” Psychol. Sci. 22, 812–820 (2011).
[CrossRef]

2010

K. Doerschner, H. Boyaci, and L. T. Maloney, “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(4):11, 9–11 (2010).
[CrossRef]

F. B. Leloup, M. R. Pointer, P. Dutré, and P. Hanselaer, “Geometry of illumination, luminance contrast, and gloss perception,” J. Opt. Soc. Am. A 27, 2046–2054 (2010).
[CrossRef]

I. Motoyoshi, “Highlight-shading relationship as a cue for the perception of translucent and transparent materials,” J. Vis. 10(9):6, 1–11 (2010).
[CrossRef]

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

J. Kim and B. L. Anderson, “Image statistics and the perception of surface gloss and lightness,” J. Vis. 10(9):3, 1–17 (2010).
[CrossRef]

M. W. A. Wijntjes and S. C. Pont, “Illusory gloss on Lambertian surfaces,” J. Vis. 10(9):13, 1–12 (2010).
[CrossRef]

2009

B. L. Anderson and J. Kim, “Image statistics do not explain the perception of gloss and lightness,” J. Vis. 9(11):10, 1–17 (2009).
[CrossRef]

D. Wollschläger and B. L. Anderson, “The role of layered scene representations in color appearance,” Curr. Biol. 19, 430–435 (2009).
[CrossRef]

2008

G. Wendt, F. Faul, and R. Mausfeld, “Highlight disparity contributes to the authenticity and strength of perceived glossiness,” J. Vis. 8(1):14, 1–10 (2008).
[CrossRef]

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

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]

Y.-X. Ho, M. S. Landy, and L. T. Maloney, “Conjoint measurement of gloss and surface texture,” Psychol. Sci. 19, 196–204 (2008).
[CrossRef]

B. L. Anderson and J. Winawer, “Layered image representations and the computation of surface lightness,” J. Vis. 8(7):18, 1–22 (2008).
[CrossRef]

F. A. A. Kingdom, “Perceiving light versus material,” Vis. Res. 48, 2090–2105 (2008).
[CrossRef]

2007

I. Motoyoshi and F. A. A. Kingdom, “Differential roles of contrast polarity reveal two streams of second-order visual processing,” Vis. Res. 47, 2047–2054 (2007).
[CrossRef]

M. S. Landy, “Visual perception: a gloss on surface properties,” Nature 447, 158–159 (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]

2006

A. D. Logvinenko and L. T. Maloney, “The proximity structure of achromatic surface colors and the impossibility of asymmetric lightness matching,” Percept. Psychophys. 68, 76–83 (2006).
[CrossRef]

S. Zdravković, E. Economou, and A. Gilchrist, “Lightness of an object under two illumination levels,” Perception 35, 1185–1201 (2006).
[CrossRef]

H. T. Nefs, J. J. Koenderink, and A. M. L. Kappers, “Shape-from-shading for matte and glossy objects,” Acta Psychol. 121, 297–316 (2006).
[CrossRef]

S. C. Pont and S. F. te Pas, “Material-illumination ambiguities and the perception of solid objects,” Perception 35, 1331–1350 (2006).
[CrossRef]

2005

S. C. Pont and J. J. Koenderink, “Reflectance from locally glossy thoroughly pitted surfaces,” Comput. Vis. Image Underst. 98, 211–222 (2005).
[CrossRef]

B. L. Anderson and J. Winawer, “Image segmentation and lightness perception,” Nature 434, 79–83 (2005).
[CrossRef]

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

R. W. Fleming and H. H. Bülthoff, “Low-level image cues in the perception of translucent materials,” ACM Trans. Appl. Percept. 2, 346–382 (2005).

2004

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. W. Fleming, A. Torralba, and E. H. Adelson, “Specular reflections and the perception of shape,” J. Vis. 4(9):10, 798–820 (2004).
[CrossRef]

V. Ekroll, F. Faul, and R. Niederée, “The peculiar nature of simultaneous colour contrast in uniform surrounds,” Vis. Res. 44, 1765–1786 (2004).
[CrossRef]

2003

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]

B. L. Anderson, “The role of occlusion in the perception of depth, lightness, and opacity,” Psychol. Rev. 110, 785–801 (2003).
[CrossRef]

2002

V. Ekroll, F. Faul, R. Niederée, and E. Richter, “The natural center of chromaticity space is not always achromatic: a new look at color induction,” Proc. Natl. Acad. Sci. USA 99, 13352–13356 (2002).
[CrossRef]

B. L. Anderson, M. Singh, and R. W. Fleming, “The interpolation of object and surface structure,” Cognit. Psychol. 44, 148–190 (2002).
[CrossRef]

M. D. Rutherford and D. H. Brainard, “Lightness constancy: a direct test of the illumination-estimation hypothesis,” Psychol. Sci. 13, 142–149 (2002).
[CrossRef]

M. Singh and B. L. Anderson, “Perceptual assignment of opacity to translucent surfaces: the role of image blur,” Perception 31, 531–552 (2002).
[CrossRef]

F. Faul and V. Ekroll, “Psychophysical model of chromatic perceptual transparency based on substractive color mixture,” J. Opt. Soc. Am. A 19, 1084–1095 (2002).
[CrossRef]

2001

J. A. Ferwerda, F. Pellacini, and D. P. Greenberg, “A psychophysically-based model of surface gloss perception,” Proc. SPIE 4299, 291–301 (2001).
[CrossRef]

1999

A. Gilchrist, C. Kossyfidis, F. Bonato, T. Agostini, J. Cataliotti, X. Li, B. Spehar, V. Annan, and E. Economou, “An anchoring theory of lightness perception,” Psychol. Rev. 106, 795–834 (1999).
[CrossRef]

B. L. Anderson, “Stereoscopic surface perception,” Neuron 24, 919–928 (1999).
[CrossRef]

1998

L. Pessoa, E. Thompson, and A. Noë, “Finding out about filling-in: a guide to perceptual completion for visual science and the philosophy of perception,” Behav. Brain Sci. 21, 723–748 (1998).

1997

B. L. Anderson, “A theory of illusory lightness and transparency in monocular and binocular images: the role of contour junctions,” Perception 26, 419–453 (1997).
[CrossRef]

1993

E. H. Adelson, “Perceptual organization and the judgment of brightness,” Science 262, 2042–2044 (1993).
[CrossRef]

1990

1985

T. Poggio, V. Torre, and C. Koch, “Computational vision and regularization theory,” Nature 317, 314–319 (1985).
[CrossRef]

1983

A. L. Gilchrist, S. Delman, and A. Jacobsen, “The classification and integration of edges as critical to the perception of reflectance and illumination,” Percept. Psychophys. 33, 425–436 (1983).
[CrossRef]

1981

J. Beck and S. Prazdny, “Highlights and the perception of glossiness,” Percept. Psychophys. 30, 407–410 (1981).
[CrossRef]

1977

A. L. Gilchrist, “Perceived lightness depends on perceived spatial arrangement,” Science 195, 185–187 (1977).
[CrossRef]

1974

F. Metelli, “The perception of transparency,” Sci. Am. 230, 90–98 (1974).
[CrossRef]

1971

1964

J. Beck, “The effect of gloss on perceived lightness,” Am. J. Psychol. 77, 54–63 (1964).
[CrossRef]

1959

E. Stewart, “The Gelb effect,” J. Exp. Psychol. 57, 235–242 (1959).
[CrossRef]

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. W. Fleming, A. Torralba, and E. H. Adelson, “Specular reflections and the perception of shape,” J. Vis. 4(9):10, 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]

E. H. Adelson, “Perceptual organization and the judgment of brightness,” Science 262, 2042–2044 (1993).
[CrossRef]

E. H. Adelson, “Lightness perception and lightness illusions,” in The New Cognitive Neurosciences, M. Gazzaniga, ed. (MIT, 2000), pp. 339–352.

Agostini, T.

A. Gilchrist, C. Kossyfidis, F. Bonato, T. Agostini, J. Cataliotti, X. Li, B. Spehar, V. Annan, and E. Economou, “An anchoring theory of lightness perception,” Psychol. Rev. 106, 795–834 (1999).
[CrossRef]

Anderson, B. L.

J. Kim, P. J. Marlow, and B. L. Anderson, “The dark side of gloss,” Nat. Neurosci. 15, 1590–1595 (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]

P. Marlow, J. Kim, and B. L. Anderson, “The role of brightness and orientation congruence in the perception of surface gloss,” J. Vis. 11(9):16, 1–12 (2011).
[CrossRef]

B. L. Anderson, “Visual perception of materials and surfaces,” Curr. Biol. 21, R978–R983 (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, 1–19 (2011).
[CrossRef]

J. Kim and B. L. Anderson, “Image statistics and the perception of surface gloss and lightness,” J. Vis. 10(9):3, 1–17 (2010).
[CrossRef]

B. L. Anderson and J. Kim, “Image statistics do not explain the perception of gloss and lightness,” J. Vis. 9(11):10, 1–17 (2009).
[CrossRef]

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Figures (7)

Fig. 1.
Fig. 1.

Relationship between surface gloss/lightness and image skewness. A matte image (a) was manipulated to generate a glossy image (b) by positively skewing its associated luminance histogram while keeping its mean luminance equal to that of the matte image [26]. The insets depict the associated luminance histograms (normalized to unity on both the frequency and luminance axes): vertical blue lines represent equal mean luminance values, and red lines unequal mode (most frequent) luminance values. The transformed surface appears glossier and blacker than the original image. The theory developed in this article explains how surface gloss and lightness are represented by the visual system, without positing that these attributes constitute perceptual dimensions corresponding to specular and diffuse reflectance, respectively. (c) and (d) Gloss maps, computed according to the model detailed in Sections 3 and 4, corresponding to the images shown in (a) and (b). Yellow areas indicate relatively high gloss, whereas dark blue areas indicate relatively low gloss. Lightness maps are not shown as they have constant values over the entirety of each image. Component (a) was modified with permission from [26].

Fig. 2.
Fig. 2.

Representation of achromatic surface colors according to gamut relativity. (a) Surface colors represented under the assumption of a single illumination level over a planar arrangement of surfaces, such as coordinates s(A)=[ϕ(A),ψ(A)] and s(B)=[ϕ(B),ψ(B)], fall on a negatively sloped gamut line in blackness–whiteness space. (b) Surface colors represented under the assumption of two different illumination intensity levels over a corrugated arrangement of surfaces, such as coordinates s(A)=[ϕ(A),ψ(A)] and c(B)=[ϕ(B),ψ(B)], fall on two different gamut lines. The inset figures in (a) and (b) perceptually illustrate how identical sets of luminance values can be parsed according to the assumptions of uniform or variable illumination levels, respectively. In (a), pictorial image cues indicate that the upper and lower rows of squares (sets A and B) lie in the same depth plane, favoring the assumption of uniform illumination over all squares [16,17,65]. Vertical pairs of squares are thus mapped to different blackness coordinates, ϕ(A)ϕ(B). As blackness coordinates constitute the computational correlate of diffuse reflectance in gamut relativity, squares in sets A and B appear to have different diffuse reflectance values. In (b), the same sets of luminance values shown in the two rows in (a) are now pictorially depicted to lie in different depth planes (the repetition of rows here enhances this depiction), favoring the assumption of variable illumination [12,16,17,60,65]. Vertical columns of squares in this arrangement are mapped to the same blackness coordinates, ϕ(A)=ϕ(B), and thus appear to have the same diffuse reflectance values. For expositional simplicity, I assume a unity scaling of blackness and whiteness axes and thus omit numerical labels above. Figure modified from [55]. Copyright of original figure held by Association for Research in Vision and Ophthalmology.

Fig. 3.
Fig. 3.

Layered representation of surface and illumination properties according to gamut relativity. (a) and (b) Gray filled dots represent the mapping of luminance to the standard colors, denoted s=(ϕ,ψ). The standard colors are then mapped to the comparison surface colors, c=(ϕν,ψ), by means of leftward and rightward vector shifts (white and black-headed arrows), respectively. The vertical columns of black dots in (a) and (b) represent the matte and glossy surface lightness layers evident in Figs. 1(a) and 1(b), respectively. These layers are formed by the comparison surface colors. Diffuse reflectance is mapped to vertical columns of blackness coordinates, as denoted by the vertical constraint line that represents the mapping from mode luminance to the mode blackness coordinate, ϕν. (c) and (d) Representation of illumination layers formed by the shadow/shading and highlight illumination colors, b=(ϕ+,0) and w=(0,ψ+), respectively. These layers are formed by means of leftward and downward vector shifts (red and blue-headed arrows) with respect to colors lying on the standard gamut, respectively. (e) and (f) Different comparison surface colors arranged in each vertical column “belong” to different gamut lines. Here I have labeled the “glossiest” and “shadiest” gamut lines, fc+ and fc, respectively. The standard gamut, representing matte surface colors, is labeled fs. Different separations between the parallel glossy and standard gamut lines represent different levels of surface gloss. The glossiest gamut line in (f), for instance, lies further away from the standard gamut than the glossiest gamut line in (e).

Fig. 4.
Fig. 4.

Examples of log-normal luminance distributions with different skewness but constant mean. Variations in the mode and maximum luminance values, ν and +, are used to simulate the dependence of lightness and gloss ratings on skewness in images with fixed mean luminance (μ) [26]. (a) Low skewness. (b) High skewness. The dotted and dotted–dashed lines represent luminance values corresponding to surface regions appearing in ambient illumination (ν) and under a specular highlight (+), respectively. Note that the mode luminance (ν) decreases as a function of skewness, due to the constraint that mean luminance (μ) must remain constant.

Fig. 5.
Fig. 5.

Examples of log-normal luminance distributions with different skewness but constant mode. Variations in the maximum luminance value (+) are used to simulate lightness constancy and gloss variation in images with fixed mode luminance, ν. (a) Low skewness. (b) High skewness.

Fig. 6.
Fig. 6.

Simulation of gloss and lightness ratings as a function of histogram skewness. (a) Points representing specular highlights (empty circles, s+) move up the standard gamut line with increasing skewness, whereas points representing surface regions seen in plain view (filled black circles, sν) move down the standard gamut with increasing skewness. The intersection points representing the surface lightness layer (filled gray circles, cν+) are mapped to different gamut lines defining increasing gloss levels. An example intersection is depicted with the vertical/horizontal black dashed lines. (b) and (c) The negative and positive co-variation of lightness and gloss ratings with skewness simulates the key finding in [26]. The compression of the lightness ratings relative to the gloss ratings is also consistent with the data reported in [26].

Fig. 7.
Fig. 7.

Simulation of lightness constancy as a function of specular highlight intensity. (a) The point s+ (empty circles) varies as a function of specular highlight intensity, whereas the point sν (filled black circle) remains constant. (b) and (c) Due to the invariant mapping νϕν+ and the variable mapping +ψν+, lightness ratings remain constant with respect to specular highlight intensity but gloss ratings increase.

Equations (6)

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

s=(ϕ,ψ)
ϕ=ωϕlog10kϕ
ψ=ωψlog10kψ,
Ω=1ϕνϕb,
λ+=|s+sν||sbsw|,
f=1σ2πe(lnμ)22σ2,

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