Abstract

The ModelFest Phase One dataset is a collection of luminance contrast thresholds for 43 two-dimensional monochromatic spatial patterns confined to an area of approximately two by two degrees. These data were collected by a collaboration among twelve laboratories, and were designed to provide a common database for calibration and testing of spatial vision models. Here I report fits of the ModelFest data with five models: Peak Contrast, Contrast Energy, Generalized Energy, a Gabor Channels model, and a Discrete Cosine Transform model. The Gabor Channels model provides the best fit, though the other, simpler models, with the exception of Peak Contrast, provide remarkably good fits as well. Though there are clear individual differences, regularities in the data suggest the possibility of constructing a standard observer for spatial vision.

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References

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  1. T. Carney, S. A. Klein, C. W. Tyler, A. D. Silverstein, B. Beutter, D. Levi, A. B. Watson, A. J. Reeves, A. M. Norcia, C.-C. Chen, W. Makous and M. P. Eckstein, "The development of an image/threshold database for designing and testing human vision models," Human Vision, Visual Processing, and Digital Display IX, Proc. SPIE, 3644, 542-551 (1999).
  2. A. B. Watson and J. A. Solomon, "ModelFest data: Fit of the Watson-Solomon model," Invest. Ophthalm. & Visual Science. 40, S572 (1999).
  3. A. B. Watson, "ModelFest Web Site, " http://vision.arc.nasa.gov/modelfest/, (1999).
  4. T. Carney, "ModelFest Web Site, " http://www.neurometrics.com/projects/Modelfest/IndexModelfest.htm, (1999).
  5. A. B. Watson, "Estimation of local spatial scale," J. Opt. Soc. Am. A. 4, 1579-1582 (1987).
    [CrossRef] [PubMed]
  6. A. B. Watson, M. Taylor and R. Borthwick, "Image quality and entropy masking," Human Vision, Visual Processing, and Digital Display VIII, Proc. SPIE, 3016, 2-12 (1997).
  7. A. B. Watson, H. B. Barlow and J. G. Robson, "What does the eye see best?," Nature. 302, 419-422 (1983).
    [CrossRef] [PubMed]
  8. K. R. K. Nielsen and B. A. Wandell, "Discrete analysis of spatial sensitivity models," J. Opt. Soc. Am. A. 5, 743-755 (1988).
    [CrossRef] [PubMed]
  9. C. Morrone and D. Burr, "Feature detection in human vision: A phase-dependent energy model," Proc. Roy. Soc. 235, 221-245 (1988).
    [CrossRef]
  10. J. Rovamo, O. Luntinen and R. Nasanen, "Modelling the dependence of contrast sensitivity on grating area and spatial frequency," Vision Res. 33, 2773-88 (1993).
    [CrossRef] [PubMed]
  11. R. F. Quick, "A vector magnitude model of contrast detection," Kybernetik. 16, 65-67 (1974).
    [CrossRef] [PubMed]
  12. H. Peterson, A. J. Ahumada, Jr. and A. Watson, "An Improved Detection Model for DCT Coefficient Quantization," Human Vision and Electronic Imaging, Proc. SPIE, 1913, 191-201 (1993).
  13. A. B. Watson, J. Hu, J. F. M. III and J. B. Mulligan, "Design and performance of a digital video quality metric," Human Vision, Visual Processing, and Digital Display IX, Proc. SPIE, 3644, 168-174 (1999).
  14. A. B. Watson, "DCT quantization matrices visually optimized for individual images," Human Vision, Visual Processing, and Digital Display IV, Proc. SPIE, 1913, 202-216 (1993).
  15. A. B. Watson, "DCTune Web Site, " http://vision.arc.nasa.gov/dctune, (1999).
  16. A. B. Watson and J. A. Solomon, "A model of visual contrast gain control and pattern masking," J. Opt. Soc. Am. A. 14, 2378 - 2390 (1997).
    [CrossRef]
  17. J. M. Foley, "Human luminance pattern mechanisms: masking experiments require a new model," J. Opt. Soc. Am. A. 11, 1710-1719 (1994).
    [CrossRef]
  18. G. Sclar, J. H. R. Maunsell and P. Lennie, "Coding of image contrast in central visual pathways of the macaque monkey," Vision Res. 30, 1-10 (1990).
    [CrossRef] [PubMed]
  19. A. J. Ahumada, Jr., "Simplified Vision Models for Image Quality Assessment," Society for Information Display International Symposium, SID Digest of Technical Papers, 27, 397-400 (1996).
  20. A. M. Rohaly, A. J. Ahumada, Jr. and A. B. Watson, "Object detection in natural backgrounds predicted by discrimination performance and models," Vision Res. 37, 3225-3235 (1997).
    [CrossRef]
  21. G. Wyszecki and W. S. Stiles, Color Science (John Wiley and Sons, New York, 1982).
  22. A. B. Watson and C. Ramirez, "A standard observer for spatial vision based on ModelFest data," Optical Society of America Annual Meeting, Digest of Technical Papers, In press, SuC6 (1999).
  23. J. G. Robson and N. Graham, "Probability summation and regional variation in contrast sensitivity across the visual field," Vision Res. 21, 409-418 (1981).
    [CrossRef] [PubMed]
  24. E. P. Simoncelli, W. T. Freeman, E. H. Adelson and D. J. Heeger, "Shiftable multi-scale transforms," IEEE Transactions on Information Theory, Special Issue on Wavelets. 38, 587-607 (1992).
    [CrossRef]

Other (24)

T. Carney, S. A. Klein, C. W. Tyler, A. D. Silverstein, B. Beutter, D. Levi, A. B. Watson, A. J. Reeves, A. M. Norcia, C.-C. Chen, W. Makous and M. P. Eckstein, "The development of an image/threshold database for designing and testing human vision models," Human Vision, Visual Processing, and Digital Display IX, Proc. SPIE, 3644, 542-551 (1999).

A. B. Watson and J. A. Solomon, "ModelFest data: Fit of the Watson-Solomon model," Invest. Ophthalm. & Visual Science. 40, S572 (1999).

A. B. Watson, "ModelFest Web Site, " http://vision.arc.nasa.gov/modelfest/, (1999).

T. Carney, "ModelFest Web Site, " http://www.neurometrics.com/projects/Modelfest/IndexModelfest.htm, (1999).

A. B. Watson, "Estimation of local spatial scale," J. Opt. Soc. Am. A. 4, 1579-1582 (1987).
[CrossRef] [PubMed]

A. B. Watson, M. Taylor and R. Borthwick, "Image quality and entropy masking," Human Vision, Visual Processing, and Digital Display VIII, Proc. SPIE, 3016, 2-12 (1997).

A. B. Watson, H. B. Barlow and J. G. Robson, "What does the eye see best?," Nature. 302, 419-422 (1983).
[CrossRef] [PubMed]

K. R. K. Nielsen and B. A. Wandell, "Discrete analysis of spatial sensitivity models," J. Opt. Soc. Am. A. 5, 743-755 (1988).
[CrossRef] [PubMed]

C. Morrone and D. Burr, "Feature detection in human vision: A phase-dependent energy model," Proc. Roy. Soc. 235, 221-245 (1988).
[CrossRef]

J. Rovamo, O. Luntinen and R. Nasanen, "Modelling the dependence of contrast sensitivity on grating area and spatial frequency," Vision Res. 33, 2773-88 (1993).
[CrossRef] [PubMed]

R. F. Quick, "A vector magnitude model of contrast detection," Kybernetik. 16, 65-67 (1974).
[CrossRef] [PubMed]

H. Peterson, A. J. Ahumada, Jr. and A. Watson, "An Improved Detection Model for DCT Coefficient Quantization," Human Vision and Electronic Imaging, Proc. SPIE, 1913, 191-201 (1993).

A. B. Watson, J. Hu, J. F. M. III and J. B. Mulligan, "Design and performance of a digital video quality metric," Human Vision, Visual Processing, and Digital Display IX, Proc. SPIE, 3644, 168-174 (1999).

A. B. Watson, "DCT quantization matrices visually optimized for individual images," Human Vision, Visual Processing, and Digital Display IV, Proc. SPIE, 1913, 202-216 (1993).

A. B. Watson, "DCTune Web Site, " http://vision.arc.nasa.gov/dctune, (1999).

A. B. Watson and J. A. Solomon, "A model of visual contrast gain control and pattern masking," J. Opt. Soc. Am. A. 14, 2378 - 2390 (1997).
[CrossRef]

J. M. Foley, "Human luminance pattern mechanisms: masking experiments require a new model," J. Opt. Soc. Am. A. 11, 1710-1719 (1994).
[CrossRef]

G. Sclar, J. H. R. Maunsell and P. Lennie, "Coding of image contrast in central visual pathways of the macaque monkey," Vision Res. 30, 1-10 (1990).
[CrossRef] [PubMed]

A. J. Ahumada, Jr., "Simplified Vision Models for Image Quality Assessment," Society for Information Display International Symposium, SID Digest of Technical Papers, 27, 397-400 (1996).

A. M. Rohaly, A. J. Ahumada, Jr. and A. B. Watson, "Object detection in natural backgrounds predicted by discrimination performance and models," Vision Res. 37, 3225-3235 (1997).
[CrossRef]

G. Wyszecki and W. S. Stiles, Color Science (John Wiley and Sons, New York, 1982).

A. B. Watson and C. Ramirez, "A standard observer for spatial vision based on ModelFest data," Optical Society of America Annual Meeting, Digest of Technical Papers, In press, SuC6 (1999).

J. G. Robson and N. Graham, "Probability summation and regional variation in contrast sensitivity across the visual field," Vision Res. 21, 409-418 (1981).
[CrossRef] [PubMed]

E. P. Simoncelli, W. T. Freeman, E. H. Adelson and D. J. Heeger, "Shiftable multi-scale transforms," IEEE Transactions on Information Theory, Special Issue on Wavelets. 38, 587-607 (1992).
[CrossRef]

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Figures (19)

Figure 1.
Figure 1.

ModelFest stimuli.

Figure 2.
Figure 2.

Movie of ModelFest stimulus #41 (240 Kbytes).

Figure 3.
Figure 3.

Threshold versus stimulus number for each observer.

Figure 4.
Figure 4.

Mean thresholds versus stimulus index.

Figure 5.
Figure 5.

Threshold variability versus stimulus number. Red points indicate mean within-observer standard deviation; black points are standard deviation across observers.

Figure 6.
Figure 6.

Mean and standard deviation of observer thresholds in dBB.

Figure 7.
Figure 7.

Mean and standard deviation of threshold for each stimulus in dBB.

Figure 8.
Figure 8.

Interpolation filter example. A) One-dimensional filter. B) Two dimensional filter.

Figure 9.
Figure 9.

RMS error for four models and nine observers, and the group.

Figure 10.
Figure 10.

Fit of the DCT8 model to the data of observer amn. The red points and error bars show means and standard deviations from four replications. The model prediction is shown by the black line. The filled trace at the bottom shows the prediction error. The RMS error is 1.06 dB. This is the best fit among all models and observers.

Figure 11.
Figure 11.

Interpolation filter parameters for the Contrast Energy model.

Figure 12.
Figure 12.

Estimated pooling exponent β for five models. Eight observers and the mean are shown.

Figure 13.
Figure 13.

Mean error and standard deviation for each stimulus for the Peak Contrast model.

Figure 14.
Figure 14.

Mean error for each stimulus for the Contrast Energy Model.

Figure 15.
Figure 15.

Mean error for each stimulus for the Generalized Energy model.

Figure 16.
Figure 16.

Mean error versus stimulus for the Gabor Channels model.

Figure 17.
Figure 17.

Mean error versus stimulus number for the DCT model with a block size of 16 pixels

Figure 18.
Figure 18.

Maximum absolute average error for seven models.

Figure 19.
Figure 19.

Mean error versus stimulus number for four models.

Tables (5)

Tables Icon

Table 1. Stimulus details. Parameters sx and sy are horizontal and vertical Gaussian standard deviations; bx and by are half amplitude full bandwidths in horizontal and vertical dimensions.

Tables Icon

Table 2. Details of ModelFest display.

Tables Icon

Table 3. Gabor Channels model parameters.

Tables Icon

Table 4. RMS error for each model and observer.

Tables Icon

Table 5. Chi-Square statistics for each model and observer, and for each model.

Equations (11)

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L ( g ) = L 0 ( 1 + c 127 ( g 128 ) )
V j , k = 1 I i = 1 I ( x i , j , k x - j , k ) 2
E j , k , m = ( p j , k , m x - j , k ) 2
S j , k , m = 1 I i = 1 I ( x i , j , k p j , k , m ) 2 = E j , k , m + V j , k
V k = 1 J j = 1 J V j , k
E k , m = 1 J j = 1 J E j , k , m
S k , m = 1 J j = 1 J S j , k , m = E k , m + V k
R = [ i r i β ] 1 β
RMS k , m = 1 J j = 1 J ( x - j , k p j , k , m ) 2 = E k , m .
χ k , m 2 = IJ ln ( 1 + S k , m V k ) .
χ m 2 = k = 1 K χ k , m 2 .

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