Abstract

Luminance patterns encode shape and surface structure of objects in our environment. Humans can detect gradations of 1%–2% of background luminance. Is this level of sensitivity to luminance gradations (contrast) determined by the amount of ecologically meaningful information available in natural scenes? In the first experiment, subjects discriminated natural images I from their “posterized” versions I(n), in which the number of luminance gradations was reduced to n. In the second experiment, amplified residual images Ires(n)I-I(n) were discriminated from white-noise images, which lack any luminance correlations and thus information content. Performance in the two experiments matched remarkably well. Furthermore, as a function of n, the signal detected in both experiments was well fitted by the mutual information between nearby image pixels in the residual image Ires(n). This suggests that human sensitivity to luminance contrast is optimized to extract ecologically useful information encoded by the luminance patterns of natural scenes.

© 2005 Optical Society of America

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2003

2002

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E. P. Simoncelli, B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef] [PubMed]

N. J. Dominy, P. W. Lucas, “Ecological importance of trichromatic vision to primates,” Nature 410, 363–366 (2001).
[CrossRef] [PubMed]

2000

D. J. Tolhurst, Y. Tadmor, “Discrimination of spectrally blended natural images: optimisation of the human visual system for encoding natural images,” Perception 29, 1087–1100 (2000).
[CrossRef]

1998

J. H. Van Hateren, A. van der Schaaf, “Independent component filters of natural images compared with simple cells in primary visual cortex,” Proc. R. Soc. London Ser. B 265, 359–366 (1998).
[CrossRef]

1997

D. J. Tolhurst, Y. Tadmor, “Discrimination of changes in the slopes of the amplitude spectra of natural images: band-limited contrast and psychometric functions,” Perception 26, 1011–1125 (1997).
[CrossRef] [PubMed]

D. J. Tolhurst, Y. Tadmor, “Band-limited contrast in natural images explains the detectability of changes in the amplitude spectra,” Vision Res. 37, 3203–3215 (1997).
[CrossRef]

1994

Y. Tadmor, D. J. Tolhurst, “Discrimination of changes in the second-order statistics of natural and synthetic images,” Vision Res. 34, 541–554 (1994).
[CrossRef] [PubMed]

1990

1988

D. S. Loshin, T. A. Banton, “Local contrast requirements for facial recognition in patients with central field defects,” Invest. Ophthalmol. Visual Sci. Suppl. 29, 43 (1988).

K. Purpura, E. Kaplan, R. M. Shapley, “Background light and the contrast gain of primate P and M retinal ganglion cells,” Proc. Natl. Acad. Sci. USA 85, 4534–4537 (1988).

1986

P. Wittle, “Increments and decrements: luminance discrimination,” Vision Res. 26, 1677–1691 (1986).
[CrossRef]

1984

G. S. Rubin, K. Siegel, “Recognition of low-pass filtered faces and letters,” Invest. Ophthalmol. Visual Sci. Suppl. 25, 71 (1984).

1983

G. E. Legge, D. Kersten, “Light and dark bars; contrast discrimination,” Vision Res. 23, 473–483 (1983).
[CrossRef] [PubMed]

1981

H. B. Barlow, “The Ferrier Lecture 1980, ‘Critical limiting factors in the design of the eye and visual cortex’,” Proc. R. Soc. London Ser. B 212, 1–34 (1981).
[CrossRef]

1970

J. V. Gaven, J. Tavitian, “The informative value of sampled images as a function of the number of gray levels used in encoding the images,” Photograph. Sci. Eng. 14, 16–21 (1970).

1969

H. B. Barlow, W. R. Levick, “Three factors limiting the reliable detection of light by retinal ganglion cells of a cat,” J. Physiol. (London) 200, 1–24 (1969).

1968

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

1967

J. C. Bartleson, R. F. Witzel, “Source coding of image information,” Photograph. Sci. Eng. 11, 263–265 (1967).

1966

G. H. Jacobs, “Responses of the lateral geniculate nucleus to light increment and decrement and the encoding of brightness,” Vision Res. 6, 83–87 (1966).
[CrossRef] [PubMed]

1965

F. W. Campbell, D. G. Green, “Optical and retinal factors affecting visual resolution,” J. Physiol. (London) 181, 576–593 (1965).

Banton, T. A.

D. S. Loshin, T. A. Banton, “Local contrast requirements for facial recognition in patients with central field defects,” Invest. Ophthalmol. Visual Sci. Suppl. 29, 43 (1988).

Barlow, H. B.

H. B. Barlow, “The Ferrier Lecture 1980, ‘Critical limiting factors in the design of the eye and visual cortex’,” Proc. R. Soc. London Ser. B 212, 1–34 (1981).
[CrossRef]

H. B. Barlow, W. R. Levick, “Three factors limiting the reliable detection of light by retinal ganglion cells of a cat,” J. Physiol. (London) 200, 1–24 (1969).

Bartleson, J. C.

J. C. Bartleson, R. F. Witzel, “Source coding of image information,” Photograph. Sci. Eng. 11, 263–265 (1967).

Bex, P. J.

Campbell, F. W.

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

F. W. Campbell, D. G. Green, “Optical and retinal factors affecting visual resolution,” J. Physiol. (London) 181, 576–593 (1965).

Dominy, N. J.

N. J. Dominy, P. W. Lucas, “Ecological importance of trichromatic vision to primates,” Nature 410, 363–366 (2001).
[CrossRef] [PubMed]

Gaven, J. V.

J. V. Gaven, J. Tavitian, “The informative value of sampled images as a function of the number of gray levels used in encoding the images,” Photograph. Sci. Eng. 14, 16–21 (1970).

Green, D. G.

F. W. Campbell, D. G. Green, “Optical and retinal factors affecting visual resolution,” J. Physiol. (London) 181, 576–593 (1965).

Jacobs, G. H.

G. H. Jacobs, “Responses of the lateral geniculate nucleus to light increment and decrement and the encoding of brightness,” Vision Res. 6, 83–87 (1966).
[CrossRef] [PubMed]

Kaplan, E.

K. Purpura, E. Kaplan, R. M. Shapley, “Background light and the contrast gain of primate P and M retinal ganglion cells,” Proc. Natl. Acad. Sci. USA 85, 4534–4537 (1988).

Kersten, D.

G. E. Legge, D. Kersten, “Light and dark bars; contrast discrimination,” Vision Res. 23, 473–483 (1983).
[CrossRef] [PubMed]

Legge, G. E.

G. E. Legge, D. Kersten, “Light and dark bars; contrast discrimination,” Vision Res. 23, 473–483 (1983).
[CrossRef] [PubMed]

Levick, W. R.

H. B. Barlow, W. R. Levick, “Three factors limiting the reliable detection of light by retinal ganglion cells of a cat,” J. Physiol. (London) 200, 1–24 (1969).

Loshin, D. S.

D. S. Loshin, T. A. Banton, “Local contrast requirements for facial recognition in patients with central field defects,” Invest. Ophthalmol. Visual Sci. Suppl. 29, 43 (1988).

Lucas, P. W.

N. J. Dominy, P. W. Lucas, “Ecological importance of trichromatic vision to primates,” Nature 410, 363–366 (2001).
[CrossRef] [PubMed]

Makous, W.

Michelson, A. A.

A. A. Michelson, Studies in Optics (University of Chicago Press, Chicago, Ill., 1927).

Olshausen, B. A.

E. P. Simoncelli, B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef] [PubMed]

Peli, E.

Petrov, Y.

Purpura, K.

K. Purpura, E. Kaplan, R. M. Shapley, “Background light and the contrast gain of primate P and M retinal ganglion cells,” Proc. Natl. Acad. Sci. USA 85, 4534–4537 (1988).

Robson, J. G.

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

Rubin, G. S.

G. S. Rubin, K. Siegel, “Recognition of low-pass filtered faces and letters,” Invest. Ophthalmol. Visual Sci. Suppl. 25, 71 (1984).

Shapley, R. M.

K. Purpura, E. Kaplan, R. M. Shapley, “Background light and the contrast gain of primate P and M retinal ganglion cells,” Proc. Natl. Acad. Sci. USA 85, 4534–4537 (1988).

Siegel, K.

G. S. Rubin, K. Siegel, “Recognition of low-pass filtered faces and letters,” Invest. Ophthalmol. Visual Sci. Suppl. 25, 71 (1984).

Simoncelli, E. P.

E. P. Simoncelli, B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef] [PubMed]

Tadmor, Y.

D. J. Tolhurst, Y. Tadmor, “Discrimination of spectrally blended natural images: optimisation of the human visual system for encoding natural images,” Perception 29, 1087–1100 (2000).
[CrossRef]

D. J. Tolhurst, Y. Tadmor, “Discrimination of changes in the slopes of the amplitude spectra of natural images: band-limited contrast and psychometric functions,” Perception 26, 1011–1125 (1997).
[CrossRef] [PubMed]

D. J. Tolhurst, Y. Tadmor, “Band-limited contrast in natural images explains the detectability of changes in the amplitude spectra,” Vision Res. 37, 3203–3215 (1997).
[CrossRef]

Y. Tadmor, D. J. Tolhurst, “Discrimination of changes in the second-order statistics of natural and synthetic images,” Vision Res. 34, 541–554 (1994).
[CrossRef] [PubMed]

Tavitian, J.

J. V. Gaven, J. Tavitian, “The informative value of sampled images as a function of the number of gray levels used in encoding the images,” Photograph. Sci. Eng. 14, 16–21 (1970).

Tolhurst, D. J.

D. J. Tolhurst, Y. Tadmor, “Discrimination of spectrally blended natural images: optimisation of the human visual system for encoding natural images,” Perception 29, 1087–1100 (2000).
[CrossRef]

D. J. Tolhurst, Y. Tadmor, “Band-limited contrast in natural images explains the detectability of changes in the amplitude spectra,” Vision Res. 37, 3203–3215 (1997).
[CrossRef]

D. J. Tolhurst, Y. Tadmor, “Discrimination of changes in the slopes of the amplitude spectra of natural images: band-limited contrast and psychometric functions,” Perception 26, 1011–1125 (1997).
[CrossRef] [PubMed]

Y. Tadmor, D. J. Tolhurst, “Discrimination of changes in the second-order statistics of natural and synthetic images,” Vision Res. 34, 541–554 (1994).
[CrossRef] [PubMed]

van der Schaaf, A.

J. H. Van Hateren, A. van der Schaaf, “Independent component filters of natural images compared with simple cells in primary visual cortex,” Proc. R. Soc. London Ser. B 265, 359–366 (1998).
[CrossRef]

Van Hateren, J. H.

J. H. Van Hateren, A. van der Schaaf, “Independent component filters of natural images compared with simple cells in primary visual cortex,” Proc. R. Soc. London Ser. B 265, 359–366 (1998).
[CrossRef]

Wittle, P.

P. Wittle, “Increments and decrements: luminance discrimination,” Vision Res. 26, 1677–1691 (1986).
[CrossRef]

Witzel, R. F.

J. C. Bartleson, R. F. Witzel, “Source coding of image information,” Photograph. Sci. Eng. 11, 263–265 (1967).

Zhaoping, L.

Annu. Rev. Neurosci.

E. P. Simoncelli, B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef] [PubMed]

Invest. Ophthalmol. Visual Sci. Suppl.

G. S. Rubin, K. Siegel, “Recognition of low-pass filtered faces and letters,” Invest. Ophthalmol. Visual Sci. Suppl. 25, 71 (1984).

D. S. Loshin, T. A. Banton, “Local contrast requirements for facial recognition in patients with central field defects,” Invest. Ophthalmol. Visual Sci. Suppl. 29, 43 (1988).

J. Opt. Soc. Am. A

J. Physiol. (London)

F. W. Campbell, D. G. Green, “Optical and retinal factors affecting visual resolution,” J. Physiol. (London) 181, 576–593 (1965).

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

H. B. Barlow, W. R. Levick, “Three factors limiting the reliable detection of light by retinal ganglion cells of a cat,” J. Physiol. (London) 200, 1–24 (1969).

Nature

N. J. Dominy, P. W. Lucas, “Ecological importance of trichromatic vision to primates,” Nature 410, 363–366 (2001).
[CrossRef] [PubMed]

Perception

D. J. Tolhurst, Y. Tadmor, “Discrimination of spectrally blended natural images: optimisation of the human visual system for encoding natural images,” Perception 29, 1087–1100 (2000).
[CrossRef]

D. J. Tolhurst, Y. Tadmor, “Discrimination of changes in the slopes of the amplitude spectra of natural images: band-limited contrast and psychometric functions,” Perception 26, 1011–1125 (1997).
[CrossRef] [PubMed]

Photograph. Sci. Eng.

J. C. Bartleson, R. F. Witzel, “Source coding of image information,” Photograph. Sci. Eng. 11, 263–265 (1967).

J. V. Gaven, J. Tavitian, “The informative value of sampled images as a function of the number of gray levels used in encoding the images,” Photograph. Sci. Eng. 14, 16–21 (1970).

Proc. Natl. Acad. Sci. USA

K. Purpura, E. Kaplan, R. M. Shapley, “Background light and the contrast gain of primate P and M retinal ganglion cells,” Proc. Natl. Acad. Sci. USA 85, 4534–4537 (1988).

Proc. R. Soc. London Ser. B

J. H. Van Hateren, A. van der Schaaf, “Independent component filters of natural images compared with simple cells in primary visual cortex,” Proc. R. Soc. London Ser. B 265, 359–366 (1998).
[CrossRef]

H. B. Barlow, “The Ferrier Lecture 1980, ‘Critical limiting factors in the design of the eye and visual cortex’,” Proc. R. Soc. London Ser. B 212, 1–34 (1981).
[CrossRef]

Vision Res.

D. J. Tolhurst, Y. Tadmor, “Band-limited contrast in natural images explains the detectability of changes in the amplitude spectra,” Vision Res. 37, 3203–3215 (1997).
[CrossRef]

Y. Tadmor, D. J. Tolhurst, “Discrimination of changes in the second-order statistics of natural and synthetic images,” Vision Res. 34, 541–554 (1994).
[CrossRef] [PubMed]

G. E. Legge, D. Kersten, “Light and dark bars; contrast discrimination,” Vision Res. 23, 473–483 (1983).
[CrossRef] [PubMed]

P. Wittle, “Increments and decrements: luminance discrimination,” Vision Res. 26, 1677–1691 (1986).
[CrossRef]

G. H. Jacobs, “Responses of the lateral geniculate nucleus to light increment and decrement and the encoding of brightness,” Vision Res. 6, 83–87 (1966).
[CrossRef] [PubMed]

Other

A. A. Michelson, Studies in Optics (University of Chicago Press, Chicago, Ill., 1927).

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Figures (4)

Fig. 1
Fig. 1

Contrast resolution and information loss. “Lena” image at different pixel depths b (contrast resolutions n) is shown on the left. The number of gray levels grows as 2 b . Residual difference from the full-resolution ( b = 8 ) image is shown on the right (contrast was amplified). Stacked black disks illustrate which bits from the full-resolution image are retained in each case.

Fig. 2
Fig. 2

Experimental stimuli. (a) The set of 12 images used in this study. Each image was subdivided into 24 256 × 256 fragments (illustrated with the white square in the left column), which were used as experimental stimuli. (b) In experiment 1, contrast resolution of a fragment was reduced by zeroing all but b highest-order bits for each pixel ( b = 3 for the case shown here). The resulting image (test) had to be discriminated from the original (reference). (c) In experiment 2, the b higher-order bits were zeroed and the residual image contrast amplified. The task was to discriminate the resulting image (test) from a white-noise image of the same pixel depth (reference).

Fig. 3
Fig. 3

Performance in contrast discrimination task (experiment 1, squares) and correlation detection task (experiment 2, circles). Pixel depth is plotted along the bottom x axis, the corresponding number of gray levels along the top x axis. Each datum shows performance averaged over the 288 image fragments. The error bars give the standard error of the mean computed from the binomial distribution. The fit (solid curves) was computed by using the mutual information measure shown in Fig. 4.

Fig. 4
Fig. 4

Mutual information between nearby image pixels as a function of pixel depth. Mutual information terms due to correlation between two pixels M 2 (circles) and three-pixels M 3 (squares, excludes M 2 terms) provide principal inputs to the total mutual information23; combined, they can be used as a measure of luminance correlations between nearby pixels.

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