Abstract

In order to test a model of peripheral vision, various contrast sensitivity functions (CSF’s) and fundamental eccentricity constants (FEC’s) [see J. Opt. Soc. Am. A 8, 1762 (1991)] were applied to real-world, wide-field (6.4°–32° eccentricity) images. The FEC is used to model the change in contrast sensitivity as a function of retinal eccentricity. The processed test images were tested perceptually by determining the threshold FEC for which the observers could discriminate the test images from the original image. It was expected that higher CSF sensitivity would be associated with higher FEC’s; and in fact, for images processed with low-pass (variable-window stimuli) CSF’s, the threshold FEC’s were larger for the higher-sensitivity (pattern-detection) CSF than for the lower-sensitivity (orientation detection) CSF. When two higher-sensitivity CSF’s were compared, the bandpass (constant-window stimuli) CSF resulted in essentially the same FEC threshold as did the low-pass (variable-window stimuli) CSF. The fact that the FEC compensated for complex differences in the form of the CSF suggested that the discrimination task was mediated by a limited range of spatial frequencies over which the two CSF’s were similar. Image contrast was then varied in order to extend the range of spatial frequencies tested. The FEC’s estimated with the lower-contrast test images were unchanged for test images obtained with the high-sensitivity, bandpass CSF but increased for test images obtained with the high-sensitivity, low-pass CSF. These results suggest that peripheral contrast sensitivity as used in the present discrimination task is based on a high-sensitivity, bandpass CSF. The peripheral-vision model validated by the present analysis has practical applications in the evaluation of wide-field simulator images as well as area-of-interest or other foveating systems.

© 2001 Optical Society of America

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References

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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. (KbH) 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 Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 301–303.
  5. J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17(4), 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), 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, 2032–2040 (1990).
    [CrossRef] [PubMed]
  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 Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 615–618.
  11. E. Peli, J. Yang, R. Goldstein, “Image invariance with changes in size: the role of peripheral contrast thresholds,” J. Opt. Soc. Am. A 8, 1762–1774 (1991).
    [CrossRef] [PubMed]
  12. E. Peli, “Test of a model of foveal vision by using simulations,” J. Opt. Soc. Am. A 13, 1131–1138 (1996).
    [CrossRef]
  13. E. Peli, “Contrast sensitivity function and image discrimination,” J. Opt. Soc. Am. A 18, 283–293 (2001).
    [CrossRef]
  14. E. L. Schwartz, “Spatial mapping in the primate sensory projection: analytic structure and relevance to perception,” Biol. Cybern. 25, 181–194 (1977).
    [CrossRef] [PubMed]
  15. A. B. Watson, “Estimation of local spatial scale,” J. Opt. Soc. Am. A 4, 1579–1582 (1987).
    [CrossRef] [PubMed]
  16. A. Johnston, “Spatial scaling of central and peripheral contrast-sensitivity functions,” J. Opt. Soc. Am. A 4, 1583–1593 (1987).
    [CrossRef] [PubMed]
  17. J. S. Pointer, R. F. Hess, “The contrast sensitivity gradient across the human visual field with emphasis on the low spatial frequency range,” Vision Res. 29, 1133–1151 (1989).
    [CrossRef]
  18. M. W. Cannon, “Perceived contrast in the fovea and periphery,” J. Opt. Soc. Am. A 2, 1760–1768 (1985).
    [CrossRef] [PubMed]
  19. W. Weibull, “A statistical distribution function of wide applicability,” J. Appl. Mech. 18, 293–297 (1951).
  20. The curve fitting was done with the SigmaPlot Scientific Graph System (Jandel Scientific).
  21. E. Peli, G. Geri, “Putting simulations of peripheral vision to the test,” Invest. Ophthalmol. Visual Sci. 34 (ARVO Suppl.), 820 (1993).
  22. E. Peli, “Simulating normal and low vision,” in Vision Models for Target Detection and Recognition, E. Peli, ed. (World Scientific, Singapore, 1995), pp. 63–87.
  23. E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vis. 7, 1–14 (1993).
    [CrossRef]
  24. A. B. Watson, “The cortex transform: rapid computation of simulated neural images,” Comput. Vision Graph. Image Process. 39, 311–327 (1987).
    [CrossRef]
  25. Under the HS/BP condition, the psychometric function for C=0.1was found to have a shape different from those obtained at the other contrast levels. The slope of the psychometric function may be related to the underlying signal to noise level, and thus it is not surprising that reducing the image contrast resulted in a flatter function with little or no change in the threshold FEC value.
  26. J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).
  27. W. S. Geisler, J. S. Perry, “A real-time foveated multi-resolution system for low-bandwidth video communication,” in Human Vision and Electronic Imaging III, B. Rogowitz, T. Pappas, eds., Proc. SPIE3299, 294–305 (1998).
    [CrossRef]

2001 (1)

1998 (1)

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

1996 (1)

1993 (3)

E. Peli, G. Geri, “Putting simulations of peripheral vision to the test,” Invest. Ophthalmol. Visual Sci. 34 (ARVO Suppl.), 820 (1993).

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

J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17(4), 14–16 (1993).

1991 (2)

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, J. Yang, R. Goldstein, “Image invariance with changes in size: the role of peripheral contrast thresholds,” J. Opt. Soc. Am. A 8, 1762–1774 (1991).
[CrossRef] [PubMed]

1990 (1)

1989 (1)

J. S. Pointer, R. F. Hess, “The contrast sensitivity gradient across the human visual field with emphasis on the low spatial frequency range,” Vision Res. 29, 1133–1151 (1989).
[CrossRef]

1987 (3)

1985 (1)

1981 (1)

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

1977 (1)

E. L. Schwartz, “Spatial mapping in the primate sensory projection: analytic structure and relevance to perception,” Biol. Cybern. 25, 181–194 (1977).
[CrossRef] [PubMed]

1951 (1)

W. Weibull, “A statistical distribution function of wide applicability,” J. Appl. Mech. 18, 293–297 (1951).

Arend, L.

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

Bradley, A.

L. N. Thibos, A. Bradley, “The limits of performance in central and peripheral vision,” in Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 301–303.

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).

Cannon, M. W.

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]

Derefeldt, G.

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

Duval-Destin, M.

M. Duval-Destin, “A spatio-temporal complete description of contrast,” in Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 615–618.

Geisler, W. S.

W. S. Geisler, J. S. Perry, “A real-time foveated multi-resolution system for low-bandwidth video communication,” in Human Vision and Electronic Imaging III, B. Rogowitz, T. Pappas, eds., Proc. SPIE3299, 294–305 (1998).
[CrossRef]

Geri, G.

E. Peli, G. Geri, “Putting simulations of peripheral vision to the test,” Invest. Ophthalmol. Visual Sci. 34 (ARVO Suppl.), 820 (1993).

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 Vis. 7, 1–14 (1993).
[CrossRef]

E. Peli, J. Yang, R. Goldstein, “Image invariance with changes in size: the role of peripheral contrast thresholds,” J. Opt. Soc. Am. A 8, 1762–1774 (1991).
[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).

Hess, R. F.

J. S. Pointer, R. F. Hess, “The contrast sensitivity gradient across the human visual field with emphasis on the low spatial frequency range,” Vision Res. 29, 1133–1151 (1989).
[CrossRef]

Hood, S. M.

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

Johnston, A.

Larimer, J.

J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17(4), 14–16 (1993).

Lauritzen, J. S.

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

Lennerstrand, G.

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

Nyberg, S.

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

Pelah, A.

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

Peli, E.

E. Peli, “Contrast sensitivity function and image discrimination,” J. Opt. Soc. Am. A 18, 283–293 (2001).
[CrossRef]

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

E. Peli, G. Geri, “Putting simulations of peripheral vision to the test,” Invest. Ophthalmol. Visual Sci. 34 (ARVO Suppl.), 820 (1993).

E. Peli, L. Arend, G. Young, R. Goldstein, “Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation,” Spatial Vis. 7, 1–14 (1993).
[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, J. Yang, R. Goldstein, “Image invariance with changes in size: the role of peripheral contrast thresholds,” J. Opt. Soc. Am. A 8, 1762–1774 (1991).
[CrossRef] [PubMed]

E. Peli, “Contrast in complex images,” J. Opt. Soc. Am. A 7, 2032–2040 (1990).
[CrossRef] [PubMed]

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

Pelli, D.

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

Perry, J. S.

W. S. Geisler, J. S. Perry, “A real-time foveated multi-resolution system for low-bandwidth video communication,” in Human Vision and Electronic Imaging III, B. Rogowitz, T. Pappas, eds., Proc. SPIE3299, 294–305 (1998).
[CrossRef]

Pointer, J. S.

J. S. Pointer, R. F. Hess, “The contrast sensitivity gradient across the human visual field with emphasis on the low spatial frequency range,” Vision Res. 29, 1133–1151 (1989).
[CrossRef]

Schwartz, E. L.

E. L. Schwartz, “Spatial mapping in the primate sensory projection: analytic structure and relevance to perception,” Biol. Cybern. 25, 181–194 (1977).
[CrossRef] [PubMed]

Tadmor, Y.

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

Thibos, L. N.

L. N. Thibos, A. Bradley, “The limits of performance in central and peripheral vision,” in Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 301–303.

Tolhurst, D. J.

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

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, “Estimation of local spatial scale,” J. Opt. Soc. Am. A 4, 1579–1582 (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]

Weibull, W.

W. Weibull, “A statistical distribution function of wide applicability,” J. Appl. Mech. 18, 293–297 (1951).

Yang, J.

Young, G.

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

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. (KbH) (1)

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

Biol. Cybern. (1)

E. L. Schwartz, “Spatial mapping in the primate sensory projection: analytic structure and relevance to perception,” Biol. Cybern. 25, 181–194 (1977).
[CrossRef] [PubMed]

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. (2)

E. Peli, G. Geri, “Putting simulations of peripheral vision to the test,” Invest. Ophthalmol. Visual Sci. 34 (ARVO Suppl.), 820 (1993).

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. Appl. Mech. (1)

W. Weibull, “A statistical distribution function of wide applicability,” J. Appl. Mech. 18, 293–297 (1951).

J. Opt. Soc. Am. A (7)

NASA Tech. Briefs (1)

J. Larimer, “Designing tomorrow’s displays,” NASA Tech. Briefs 17(4), 14–16 (1993).

Perception (1)

J. S. Lauritzen, S. M. Hood, D. J. Tolhurst, Y. Tadmor, A. Pelah, “Detection of Gabor patches embedded in natural images,” Perception 27, 151–152 (1998).

Spatial Vis. (1)

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

Vision Res. (1)

J. S. Pointer, R. F. Hess, “The contrast sensitivity gradient across the human visual field with emphasis on the low spatial frequency range,” Vision Res. 29, 1133–1151 (1989).
[CrossRef]

Other (10)

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]

M. Duval-Destin, “A spatio-temporal complete description of contrast,” in Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 615–618.

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), pp. 245–283.

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 Vol. 22 of Society for Information Display Digest of Technical Papers (Society for Information Display, Santa Ana, Calif., 1991), pp. 301–303.

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

Under the HS/BP condition, the psychometric function for C=0.1was found to have a shape different from those obtained at the other contrast levels. The slope of the psychometric function may be related to the underlying signal to noise level, and thus it is not surprising that reducing the image contrast resulted in a flatter function with little or no change in the threshold FEC value.

The curve fitting was done with the SigmaPlot Scientific Graph System (Jandel Scientific).

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

W. S. Geisler, J. S. Perry, “A real-time foveated multi-resolution system for low-bandwidth video communication,” in Human Vision and Electronic Imaging III, B. Rogowitz, T. Pappas, eds., Proc. SPIE3299, 294–305 (1998).
[CrossRef]

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Figures (6)

Fig. 1
Fig. 1

Typical test stimuli used in the image discrimination study. The images shown here were obtained by applying (a) an FEC level of 0.15 to the right side of the mirror-image pair derived from the right side of the planes image and (b) an FEC level of 0.20 to the left side of the mirror-image pair derived from the left half of the airport image. Only the two highest FEC levels are depicted here since lower levels are difficult to see in printed images.

Fig. 2
Fig. 2

(a) The three CSF data sets used to produce the present test images. The low sensitivity/lowpass (LS/LP) set was obtained with Gabor stimuli in an orientation discrimination task. The high sensitivity/low-pass (HS/LP) set was obtained with the same stimuli in a contrast detection task. The high sensitivity/bandpass (HS/BP) set was obtained with fixed-aperture grating stimuli in a detection task. The HS/BP CSF was extrapolated to lower frequencies by using a straight line of slope 0.5, and to higher frequencies by extending the line segment connecting the two highest-frequency points. The spatial frequencies corresponding to the high-frequency extrapolation were not present in the test images used here. (b) The HS/LP and LS/LP CSF’s and a function that approximates a 1/f image amplitude spectrum whose contrast, C, has been designated as 1.0 (see Section 4). (c) The HS/BP and HS/LP CSF’s and a series of functions that approximate relative 1/f amplitude spectra of images whose contrasts vary by the factors shown and that were used as test stimuli in the present study (see Section 4).

Fig. 3
Fig. 3

Preliminary results reported in Peli and Geri21 and Peli.22 The smooth curves in the graph represent the best-fitting, three-parameter Weibull function [i.e., not Eq. (2), since the lower asymptote was estimated rather than fixed at 50%]. The data fall on the active part of the psychometric function, and the FEC found is close to the prediction. However, the percent correct is high even for the lowest FEC levels (top graph), and the data display a clear image dependence (bottom graph). These two aspects of the data are not in agreement with the predictions of the vision model tested here.

Fig. 4
Fig. 4

Discrimination data obtained with images from which the HFR has been removed. The smooth curves correspond to the best-fit, two-parameter Weibull function. The data are means obtained over four observers under the high-sensitivity/bandpass (HS/BP) and low-sensitivity/low-pass (LS/LP) conditions. The error bars are ±1 standard error of the mean (s.e.m.) intervals.

Fig. 5
Fig. 5

Comparison of data from test images obtained with the high-sensitivity/bandpass (HS/BP) and the high-sensitivity/low-pass (HS/LP) (top graph) or the high-sensitivity/low-pass (HS/LP) and low-sensitivity/low-pass (LS/LP) (bottom graph) CSF functions. The smooth curves correspond to the best-fit, two-parameter Weibull function. The data are means obtained over four observers, and the error bars are ±1 s.e.m. intervals.

Fig. 6
Fig. 6

Effect of image contrast on the threshold FEC level. Reducing the contrast of the test images increased the threshold FEC for the images processed with the high-sensitivity/low-pass (HS/LP) CSF but not with the high-sensitivity/bandpass (HS/BP) CSF. The error bars are ±1 s.e.m. intervals.

Tables (1)

Tables Icon

Table 1 Sensitivity and Passband Characteristics of the CSF’s Used to Process the Test Stimuli of the Present Study, and the Psychophysical Technique and Spatial Window of the Stimuli Used to Obtain Those CSF’s

Equations (2)

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

T(θ, f)=T(0, f) exp(FECθf),
P=100-50 exp-FECFs,

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