The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A standard model for foveal detection of spatial contrast
Open accessAbstract
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.
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Cites
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“On the low-frequency side, it is truncated at a value of a: SLPð f ; f 0; b; aÞ ¼ 10 log10ð f=f 0Þ b 2 ¼ 1 a f G f 0 and sLP G 1 a: ð17Þ MS This function was introduced by Mannos & Sakrison (1974) in their pioneering work on image quality.”
BackgroundQuantitative analysis of cat retinal ganglion cell response to visual stimuli
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Self-citation
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Fully Deep Blind Image Quality Predictor
Kim, J., & Lee, S. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2639328 · Cited by 459
Deep CNN-Based Blind Image Quality Predictor
Kim, J., Nguyen, A.-D., & Lee, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2829819 · Cited by 331
From circuits to behavior: a bridge too far?
Carandini, M. (2012) · Nature Neuroscience · doi:10.1038/nn.3043 · Cited by 187
Temporal Trajectory Aware Video Quality Measure
Barkowsky, M., Bialkowski, J., Eskofier, B., Bitto, R., & Kaup, A. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2015375 · Cited by 75
Legras, R., Benard, Y., & Lopez-Gil, N. (2012) · Journal of Cataract and Refractive Surgery · doi:10.1016/j.jcrs.2011.10.032 · Cited by 46
Metadata
- Authors
- Watson, A. B., & Ahumada, A. J.
- Venue
- Journal of Vision
- Published
- 2005-10-26
- Volume
- 5
- Issue
- 9
- Pages
- 6
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2044456223
- Cited by
- 329
- References
- 40
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
