A standard model for foveal detection of spatial contrast.

By Andrew B. Watson, A. Ahumada. Journal of Vision, 2005

356 citations recorded by the source corpus.

The ModelFest data set was created to provide a public source of data to test and calibrate models of foveal spatial contrast detection. It consists of contrast thresholds for 43 foveal achromatic contrast stimuli collected from each of 16 observers. We have fit these data with a variety of simple models that include one of several contrast sensitivity functions, an oblique effect, a spatial sensitivity aperture, spatial frequency channels, and nonlinear Minkowski summation. While we are able to identify one model, with particular parameters, as providing the lowest overall residual error, we also note that the differences among several good-fitting models are small. We find a strong reciprocity between the size of the spatial aperture and the value of the summation exponent: both are effective means of limiting the extent of spatial summation. The results demonstrate the power of simple models to account for the visibility of a wide variety of spatial stimuli and suggest that special mechanisms to deal with special classes of stimuli are not needed. But the results also illustrate the limited power of even this large data set to distinguish among similar competing models. We identify one model as a possible standard, suitable for simple theoretical and applied predictions.

Subfields: Cognitive Neuroscience, Ecology, Evolution, Behavior and Systematics, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/a1f9445872361df1e967b02314be20a25fc54d69

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Citations in the corpus

High Dynamic Range Imaging (2015, methodology): “The model has been shown to predict numerous discrimination data sets, such as ModelFest [171], historical Blackwell’s t.”
Deep CNN-Based Blind Image Quality Predictor (2019, background): “example, the visual sensitivity of the HVS varies according to spatial frequency of stimuli [7], [8], and the presence of texture hinders other spatially coincident image changes [9].”
HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions (2011, methodology): “The majority of the existing visual models are intended for very limited luminance ranges, usually restricted to the range available on a CRT display or print [ Daly 1993; Lubin 1995; Rohaly et al. 1997; Watson and Ahumada Jr 2005].”
Measuring contrast sensitivity (2013, unknown): “The resulting data were used to evaluate the goodness ofﬁtof5(Watson,2000)or9(Watson&Ahumada,2005)competing CSF models.”
Fully Deep Blind Image Quality Predictor (2017, background): “Contrast sensitivity function deals with variation in visual sensitivity according to spatial frequency [3].”