Abstract

A previous study tested the validity of simulations of the appearance of a natural image (from different observation distances) generated by using a visual model and contrast sensitivity functions of the individual observers [J. Opt. Soc. Am. A 13, 1131 (1996)]. Deleting image spatial-frequency components that should be undetectable made the simulations indistinguishable from the original images at distances larger than the simulated distance. The simulated observation distance accurately predicted the distance at which the simulated image could be discriminated from the original image. Owing to the 1/f characteristic of natural images’ spatial spectra, the individual contrast sensitivity functions (CSF’s) used in the simulations of the previous study were actually tested only over a narrow range of retinal spatial frequencies. To test the CSF’s over a wide range of frequencies, the same simulations and testing procedure were applied to five contrast versions of the images (10–300%). This provides a stronger test of the model, of the simulations, and specifically of the CSF’s used. The relevant CSF for a discrimination task was found to be obtained by using 1-octave Gabor stimuli measured in a contrast detection task. The relevant CSF data had to be measured over a range of observation distances, owing to limitations of the displays.

© 2001 Optical Society of America

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  1. A. P. Ginsburg, “Visual information processing based on spatial filters constrained by biological data,” Ph.D. dissertation (Cambridge University, Cambridge, UK, 1978).
  2. B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).
  3. D. Pelli, “What is low vision?” Videotape, Institute for Sensory Research, Syracuse University, Syracuse, N.Y., 1990.
  4. L. N. Thibos, A. Bradley, “The limits of performance in central and peripheral vision,” in SID ’91 Digest of Technical Papers (Society for Information Display, Playa del Rey, Calif., 1991), Vol. XXII, pp. 301–303.
  5. J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17, 14–16 (1993).
  6. J. Lubin, “A visual discrimination model for imaging system design and evaluation,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 10, pp. 245–283.
  7. E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).
  8. E. Peli, “Contrast in complex images,” J. Opt. Soc. Am. A 7, 2030–2040 (1990).
    [CrossRef]
  9. S. Daly, “The visual differences predictor: an algorithm for the assessment of image fidelity,” in Human Vision: Visual Processing, and Digital Display III, B. E. Rogowitz, ed. Proc. SPIE1666, 2–15 (1992).
    [CrossRef]
  10. M. Duval-Destin, “A spatio-temporal complete description of contrast,” in SID’91 Digest of Technical Papers (Society for Information Display, Playa del Rey, Calif., 1991), Vol. XXII, pp. 615–618.
  11. E. Peli, “Test of a model of foveal vision by using simulations,” J. Opt. Soc. Am. A 13, 1131–1138 (1996).
    [CrossRef]
  12. E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
    [CrossRef] [PubMed]
  13. E. Peli, “Simulating normal and low vision,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 3, pp. 63–87.
  14. B. R. Stephens, M. S. Banks, “The development of contrast constancy,” J. Exp. Child. Psychol. 40, 528–547 (1985).
    [CrossRef] [PubMed]
  15. N. Brady, D. J. Field, “What’s constant in contrast constancy? The effects of scaling on the perceived contrast of bandpass patterns,” Vision Res. 35, 739–756 (1995).
    [CrossRef] [PubMed]
  16. D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” J. Opt. Soc. Am. A 4, 2379–2394 (1987).
    [CrossRef] [PubMed]
  17. D. J. Tolhurst, Y. Tadmor, T. Chao, “The amplitude spectra of natural images,” Ophthalmic Physiol. Opt. 12, 229–232 (1992).
    [CrossRef] [PubMed]
  18. D. L. Ruderman, W. Bialeck, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
    [CrossRef] [PubMed]
  19. 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]
  20. 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]
  21. E. Peli, “Display nonlinearity in digital image processing for visual communications,” Opt. Eng. 31, 2374–2382 (1992).
    [CrossRef]
  22. These images were originally recorded with standard video cameras designed to display on a nonlinearized CRT. To enable a linear relationship between the displayed luminance levels and the numerical representation of the images, we presented the images using a linearizing (Gamma corrected) lookup table. To maintain the natural appearance and contrast range of the images, the original images were preprocessed to include the measured display Gamma function.21
  23. In fact, it was the amplitude, not the contrast, of the images that was increased or decreased. This operation, in which the image mean value is subtracted and the remaining values are scaled up or down, is frequently referred to as contrast increase or decrease. As noted by Peli,8the changes in contrast are equivalent to changes in amplitude only where the local luminance is equal to the mean luminance. I will use the term contrast changes here to conform to previous usage, recognizing that in many places the differences were small. This distinction has no bearing on the results or the conclusions drawn here. The contrast of an image can be changed by a fixed factor for all frequencies and locations by using a band-by-band amplification within the context of the contrast metric developed in Ref. 8.
  24. The CSF was also measured with a staircase procedure. Only the CSF measured with MOA methods was used in the simulation study. For the subjects who were well-trained psychophysics subjects, the results with MOA differed only slightly from the CSF obtained with the staircase procedure. The CSF data and the standard error of themeasurements were similar to data collected for these stimuli with different systems and with adaptive forced-choice procedures.12This was not the case for the novice subject. For this subject (JML) the staircase-procedure data was similar to the data from the other observers, but the MOA data showed substantially reduced sensitivity (as much as 0.5 log unit at middle and low frequencies), even when measured repeatedly. It is interesting to note that for this subject the MOA results provided a better prediction of the simulation performance than did the CSF obtained with the staircase procedure.
  25. A. B. Watson, “The cortex transform: rapid computation of simulated neural images,” Comput. Vision Graph. Image Process. 39, 311–327 (1987).
    [CrossRef]
  26. H. R. Wilson, “Quantitative models for pattern detection and discrimination,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 1, pp. 3–15.
  27. A. J. Ahumada, “Simplified vision models for image-quality assessment,” in SID’96 Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1996), Vol. XXVII, pp. 397–400.
  28. F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 203, 223–235 (1968).
  29. M. A. Garcia-Perez, V. Sierra-Vazquez, “Visual processing in the joint spatial/spatial-frequency domain,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 2, pp. 16–62.
  30. L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).
  31. E. Peli, M. A. Garcia-Perez, “Artifacts of CRT displays in vision research and other critical applications,” in SID 2000 Digest of Technical Papers, J. Morreale, ed. (Society for Information Display, San Jose, Calif., 2000), Vol. XXXI, pp. 396–399.
  32. J. B. Mulligan, L. S. Stone, “Halftoning method for the generation of motion stimuli,” J. Opt. Soc. Am. A 6, 1217–1227 (1989).
    [CrossRef]

1997 (1)

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]

1996 (1)

1995 (1)

N. Brady, D. J. Field, “What’s constant in contrast constancy? The effects of scaling on the perceived contrast of bandpass patterns,” Vision Res. 35, 739–756 (1995).
[CrossRef] [PubMed]

1994 (2)

D. L. Ruderman, W. Bialeck, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[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]

1993 (2)

J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17, 14–16 (1993).

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
[CrossRef] [PubMed]

1992 (2)

D. J. Tolhurst, Y. Tadmor, T. Chao, “The amplitude spectra of natural images,” Ophthalmic Physiol. Opt. 12, 229–232 (1992).
[CrossRef] [PubMed]

E. Peli, “Display nonlinearity in digital image processing for visual communications,” Opt. Eng. 31, 2374–2382 (1992).
[CrossRef]

1991 (1)

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

1990 (1)

E. Peli, “Contrast in complex images,” J. Opt. Soc. Am. A 7, 2030–2040 (1990).
[CrossRef]

1989 (1)

1987 (3)

L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).

D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” J. Opt. Soc. Am. A 4, 2379–2394 (1987).
[CrossRef] [PubMed]

A. B. Watson, “The cortex transform: rapid computation of simulated neural images,” Comput. Vision Graph. Image Process. 39, 311–327 (1987).
[CrossRef]

1985 (1)

B. R. Stephens, M. S. Banks, “The development of contrast constancy,” J. Exp. Child. Psychol. 40, 528–547 (1985).
[CrossRef] [PubMed]

1981 (1)

B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).

1968 (1)

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 203, 223–235 (1968).

Abramov, I.

L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).

Ahumada, A. J.

A. J. Ahumada, “Simplified vision models for image-quality assessment,” in SID’96 Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1996), Vol. XXVII, pp. 397–400.

Arend, L.

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
[CrossRef] [PubMed]

Banks, M. S.

B. R. Stephens, M. S. Banks, “The development of contrast constancy,” J. Exp. Child. Psychol. 40, 528–547 (1985).
[CrossRef] [PubMed]

Bialeck, W.

D. L. Ruderman, W. Bialeck, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef] [PubMed]

Bradley, A.

L. N. Thibos, A. Bradley, “The limits of performance in central and peripheral vision,” in SID ’91 Digest of Technical Papers (Society for Information Display, Playa del Rey, Calif., 1991), Vol. XXII, pp. 301–303.

Brady, N.

N. Brady, D. J. Field, “What’s constant in contrast constancy? The effects of scaling on the perceived contrast of bandpass patterns,” Vision Res. 35, 739–756 (1995).
[CrossRef] [PubMed]

Buzney, S. M.

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

Camenzuli, C.

L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).

Campbell, F. W.

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 203, 223–235 (1968).

Chao, T.

D. J. Tolhurst, Y. Tadmor, T. Chao, “The amplitude spectra of natural images,” Ophthalmic Physiol. Opt. 12, 229–232 (1992).
[CrossRef] [PubMed]

Daly, S.

S. Daly, “The visual differences predictor: an algorithm for the assessment of image fidelity,” in Human Vision: Visual Processing, and Digital Display III, B. E. Rogowitz, ed. Proc. SPIE1666, 2–15 (1992).
[CrossRef]

de Bie, J.

L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).

Derefeldt, G.

B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).

Duval-Destin, M.

M. Duval-Destin, “A spatio-temporal complete description of contrast,” in SID’91 Digest of Technical Papers (Society for Information Display, Playa del Rey, Calif., 1991), Vol. XXII, pp. 615–618.

Field, D. J.

N. Brady, D. J. Field, “What’s constant in contrast constancy? The effects of scaling on the perceived contrast of bandpass patterns,” Vision Res. 35, 739–756 (1995).
[CrossRef] [PubMed]

D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” J. Opt. Soc. Am. A 4, 2379–2394 (1987).
[CrossRef] [PubMed]

Garcia-Perez, M. A.

M. A. Garcia-Perez, V. Sierra-Vazquez, “Visual processing in the joint spatial/spatial-frequency domain,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 2, pp. 16–62.

E. Peli, M. A. Garcia-Perez, “Artifacts of CRT displays in vision research and other critical applications,” in SID 2000 Digest of Technical Papers, J. Morreale, ed. (Society for Information Display, San Jose, Calif., 2000), Vol. XXXI, pp. 396–399.

Ginsburg, A. P.

A. P. Ginsburg, “Visual information processing based on spatial filters constrained by biological data,” Ph.D. dissertation (Cambridge University, Cambridge, UK, 1978).

Goldstein, R.

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
[CrossRef] [PubMed]

Goldstein, R. B.

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

Hainline, L.

L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).

Larimer, J.

J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17, 14–16 (1993).

Lennerstrand, G.

B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).

Lubin, J.

J. Lubin, “A visual discrimination model for imaging system design and evaluation,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 10, pp. 245–283.

Lundh, B. L.

B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).

Mulligan, J. B.

Nyberg, S.

B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).

Peli, E.

E. Peli, “Test of a model of foveal vision by using simulations,” J. Opt. Soc. Am. A 13, 1131–1138 (1996).
[CrossRef]

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
[CrossRef] [PubMed]

E. Peli, “Display nonlinearity in digital image processing for visual communications,” Opt. Eng. 31, 2374–2382 (1992).
[CrossRef]

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

E. Peli, “Contrast in complex images,” J. Opt. Soc. Am. A 7, 2030–2040 (1990).
[CrossRef]

E. Peli, “Simulating normal and low vision,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 3, pp. 63–87.

E. Peli, M. A. Garcia-Perez, “Artifacts of CRT displays in vision research and other critical applications,” in SID 2000 Digest of Technical Papers, J. Morreale, ed. (Society for Information Display, San Jose, Calif., 2000), Vol. XXXI, pp. 396–399.

Pelli, D.

D. Pelli, “What is low vision?” Videotape, Institute for Sensory Research, Syracuse University, Syracuse, N.Y., 1990.

Robson, J. G.

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 203, 223–235 (1968).

Ruderman, D. L.

D. L. Ruderman, W. Bialeck, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef] [PubMed]

Sierra-Vazquez, V.

M. A. Garcia-Perez, V. Sierra-Vazquez, “Visual processing in the joint spatial/spatial-frequency domain,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 2, pp. 16–62.

Stephens, B. R.

B. R. Stephens, M. S. Banks, “The development of contrast constancy,” J. Exp. Child. Psychol. 40, 528–547 (1985).
[CrossRef] [PubMed]

Stone, L. S.

Tadmor, Y.

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]

D. J. Tolhurst, Y. Tadmor, T. Chao, “The amplitude spectra of natural images,” Ophthalmic Physiol. Opt. 12, 229–232 (1992).
[CrossRef] [PubMed]

Thibos, L. N.

L. N. Thibos, A. Bradley, “The limits of performance in central and peripheral vision,” in SID ’91 Digest of Technical Papers (Society for Information Display, Playa del Rey, Calif., 1991), Vol. XXII, pp. 301–303.

Tolhurst, D. J.

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]

D. J. Tolhurst, Y. Tadmor, T. Chao, “The amplitude spectra of natural images,” Ophthalmic Physiol. Opt. 12, 229–232 (1992).
[CrossRef] [PubMed]

Trempe, C. L.

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

Watson, A. B.

A. B. Watson, “The cortex transform: rapid computation of simulated neural images,” Comput. Vision Graph. Image Process. 39, 311–327 (1987).
[CrossRef]

Wilson, H. R.

H. R. Wilson, “Quantitative models for pattern detection and discrimination,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 1, pp. 3–15.

Young, G.

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
[CrossRef] [PubMed]

Young, G. M.

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

Acta Ophthalmol. (1)

B. L. Lundh, G. Derefeldt, S. Nyberg, G. Lennerstrand, “Picture simulation of contrast sensitivity in organic and functional amblyopia,” Acta Ophthalmol. 59, 774–783 (1981).

Clin. Vision Sci. (1)

L. Hainline, J. de Bie, I. Abramov, C. Camenzuli, “Eye movement voting: a new technique for deriving spatial contrast sensitivity,” Clin. Vision Sci. 1, 33–44 (1987).

Comput. Vision Graph. Image Process. (1)

A. B. Watson, “The cortex transform: rapid computation of simulated neural images,” Comput. Vision Graph. Image Process. 39, 311–327 (1987).
[CrossRef]

Invest. Ophthalmol. Visual Sci. (1)

E. Peli, R. B. Goldstein, G. M. Young, C. L. Trempe, S. M. Buzney, “Image enhancement for the visually impaired:simulations and experimental results,” Invest. Ophthalmol. Visual Sci. 32, 2337–2350 (1991).

J. Exp. Child. Psychol. (1)

B. R. Stephens, M. S. Banks, “The development of contrast constancy,” J. Exp. Child. Psychol. 40, 528–547 (1985).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A (4)

J. Physiol. (London) (1)

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 203, 223–235 (1968).

NASA Tech. Briefs (1)

J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17, 14–16 (1993).

Ophthalmic Physiol. Opt. (1)

D. J. Tolhurst, Y. Tadmor, T. Chao, “The amplitude spectra of natural images,” Ophthalmic Physiol. Opt. 12, 229–232 (1992).
[CrossRef] [PubMed]

Opt. Eng. (1)

E. Peli, “Display nonlinearity in digital image processing for visual communications,” Opt. Eng. 31, 2374–2382 (1992).
[CrossRef]

Phys. Rev. Lett. (1)

D. L. Ruderman, W. Bialeck, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef] [PubMed]

Spatial Vision (1)

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vision 7, 1–14 (1993).
[CrossRef] [PubMed]

Vision Res. (3)

N. Brady, D. J. Field, “What’s constant in contrast constancy? The effects of scaling on the perceived contrast of bandpass patterns,” Vision Res. 35, 739–756 (1995).
[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]

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]

Other (14)

These images were originally recorded with standard video cameras designed to display on a nonlinearized CRT. To enable a linear relationship between the displayed luminance levels and the numerical representation of the images, we presented the images using a linearizing (Gamma corrected) lookup table. To maintain the natural appearance and contrast range of the images, the original images were preprocessed to include the measured display Gamma function.21

In fact, it was the amplitude, not the contrast, of the images that was increased or decreased. This operation, in which the image mean value is subtracted and the remaining values are scaled up or down, is frequently referred to as contrast increase or decrease. As noted by Peli,8the changes in contrast are equivalent to changes in amplitude only where the local luminance is equal to the mean luminance. I will use the term contrast changes here to conform to previous usage, recognizing that in many places the differences were small. This distinction has no bearing on the results or the conclusions drawn here. The contrast of an image can be changed by a fixed factor for all frequencies and locations by using a band-by-band amplification within the context of the contrast metric developed in Ref. 8.

The CSF was also measured with a staircase procedure. Only the CSF measured with MOA methods was used in the simulation study. For the subjects who were well-trained psychophysics subjects, the results with MOA differed only slightly from the CSF obtained with the staircase procedure. The CSF data and the standard error of themeasurements were similar to data collected for these stimuli with different systems and with adaptive forced-choice procedures.12This was not the case for the novice subject. For this subject (JML) the staircase-procedure data was similar to the data from the other observers, but the MOA data showed substantially reduced sensitivity (as much as 0.5 log unit at middle and low frequencies), even when measured repeatedly. It is interesting to note that for this subject the MOA results provided a better prediction of the simulation performance than did the CSF obtained with the staircase procedure.

M. A. Garcia-Perez, V. Sierra-Vazquez, “Visual processing in the joint spatial/spatial-frequency domain,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 2, pp. 16–62.

H. R. Wilson, “Quantitative models for pattern detection and discrimination,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 1, pp. 3–15.

A. J. Ahumada, “Simplified vision models for image-quality assessment,” in SID’96 Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1996), Vol. XXVII, pp. 397–400.

E. Peli, M. A. Garcia-Perez, “Artifacts of CRT displays in vision research and other critical applications,” in SID 2000 Digest of Technical Papers, J. Morreale, ed. (Society for Information Display, San Jose, Calif., 2000), Vol. XXXI, pp. 396–399.

A. P. Ginsburg, “Visual information processing based on spatial filters constrained by biological data,” Ph.D. dissertation (Cambridge University, Cambridge, UK, 1978).

D. Pelli, “What is low vision?” Videotape, Institute for Sensory Research, Syracuse University, Syracuse, N.Y., 1990.

L. N. Thibos, A. Bradley, “The limits of performance in central and peripheral vision,” in SID ’91 Digest of Technical Papers (Society for Information Display, Playa del Rey, Calif., 1991), Vol. XXII, pp. 301–303.

E. Peli, “Simulating normal and low vision,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 3, pp. 63–87.

J. Lubin, “A visual discrimination model for imaging system design and evaluation,” in Vision Models for Target Detection, E. Peli, ed. (World Scientific, Singapore, 1995), Chap. 10, pp. 245–283.

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Figures (6)

Fig. 1
Fig. 1

(a) Schematic illustration of the interaction of image spatial-frequency content with the observer’s CSF. The thick line represents a typical image spectrum (changing as 1/f ). The transformation of spatial frequencies from units of cycles per image to units of cycles per degree is determined by the image size of 4 deg. The part of the spectrum below the observer’s CSF (detection threshold obtained with Gabor stimuli) will not be detectable, as illustrated by the change of the spectrum line from a thick to a thin line. The fixed window contrast threshold represents the CSF that was rejected by Peli’s11 study. As can be seen here, a single retinal frequency testing is sufficient to distinguish the two CSF’s. (b) A change in observation distance, which causes the image to shrink to 2 deg on the observers’ retina, shifts the corresponding image spectrum, IS, along a slope of -1.0. At the new distance lower object frequencies are removed by the observer’s CSF, but essentially the same retinal frequencies are involved. (c) The additional spectral curves represent the spatial spectra of images with increased and decreased contrast that shift the intersection of the spectra with the threshold to higher and lower retinal frequencies, respectively, permitting testing of other parts of the CSF.

Fig. 2
Fig. 2

Examples of the images used in the study. The original unprocessed versions at various contrast levels are shown in the bottom row. The columns from left to right represent images with 10%, 30%, 100%, and 300% contrast. The simulations of images spanning 1, 2, and 4 deg are shown in the first, second, and third row, respectively. The appearance of the simulations of the other scenes at 100% contrast can be found in Fig. 6(a) below.

Fig. 3
Fig. 3

Distances at which the simulated images were distinguished from the corresponding original images compared with the simulated observation distance. (a) For a well-practiced subject the data deviate from the prediction (diagonal line) only for the extreme contrast conditions corresponding to detection of low spatial frequencies (10% contrast) and high spatial frequencies (300% contrast). (b) For a novice subject the simulated images were distinguished from the original image at a distance shorter than the simulated distance (c) and (d).Similar results were obtained for two more subjects. For all subjects the deviations of different contrast lines from each other are regular and consistent.

Fig. 4
Fig. 4

CSF data measured for two subjects at different observation distances. The data collected at 2 m distance together with the illustrated extrapolations were used in the first experiment. The data shown by a solid line marked “combined CSF” was used in the simulations of the second experiment.

Fig. 5
Fig. 5

Distances at which the simulated images were distinguished from the corresponding original images compared with the simulated observation distance, for the two of the subjects in Fig. 3. Here the simulations were computed with the combined CSF’s obtained from different observation distances. (a) For the well-practiced subject the data with the combined CSF are now very close to the prediction represented by the diagonal solid line. (b) For the novice subject the practice gained in the task resulted in the simulated images here being distinguished from the original image at a distance farther than the simulated distance. In addition, the different contrast versions are detected closer to each other and closer to the prediction line than in Fig. 3(b). The dotted lines include all observation distances that deviate from the simulated distances by a factor of 2. As can be seen, all data points for one subject and most of the data for the other are included in this range.

Fig. 6
Fig. 6

Comparison of (a) the simulations (of 100% contrast versions of the images) used in this study with (b) the simulations obtained with linear filtering of the images by using the normalized CSF as the filter function. In the two cases the same contrast detection data was used and was applied band by band. The linearly filtered images are much blurrier than those used here. The linearly filtered simulations would be distinguishable at a distance much larger than the simulated observation distance and are therefore inadequate. For each scene each column and row represent the same simulations as the columns and rows in Fig. 2.

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