The DOI matches its Crossref record.
Perceptual image distortion:
Abstract
In this paper, we present a perceptual distortion measure that predicts image integrity far better than mean-squared error. This perceptual distortion measure is based on a model of human visual processing that fits empirical measurements of: (1) the response properties of neurons in the primary visual cortex, and (2) the psychophysics of spatial pattern detection. We also illustrate the usefulness of the model in measuring perceptual distortion in real images.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Contrast masking has been modeled using a mechanism of contrast gain control that often takes the form of a divisive normalization [63], [25], [64].”
From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [25]
In the citing paper
“Several HVS modeling based IQA algorithms account for contrast masking in human vision using divisive normalization models of contrast gain control [25], [64], [24].”
From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [25]
In the citing paper
“The divisive inhibition pool and divisive normalization model used here differ from other contrast gain control models. For example, Lubin models divisive inhibition within the same sub-band, while the Teo and Watson models seek to account for cross-channel inhibition [24], [25], [64].”
From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [25]
Citations
Cited by
Sorted by citations
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253
Most apparent distortion: full-reference image quality assessment and the role of strategy
Larson, E. C., & Chandler, D. M. (2010) · Journal of Electronic Imaging · doi:10.1117/1.3267105 · Cited by 1,953
An information fidelity criterion for image quality assessment using natural scene statistics
Sheikh, H. R., Bovik, A. C., & de Veciana, G. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859389 · Cited by 1,399
Video quality assessment based on structural distortion measurement
Wang, Z., Lu, L., & Bovik, A. C. (2004) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(03)00076-6 · Cited by 1,024
Metadata
- Authors
- Teo, P. C., & Heeger, D. J.
- Affiliations
- Stanford University (US)
- ORCID
- Heeger, David J.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 1994-05-01
- Volume
- 2179
- Pages
- 127-141
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2001896471
- Cited by
- 104
- References
- 0
- Citations by year
- 2026: 2 · 2023: 2 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 4 · 2017: 3 · 2016: 4 · 2015: 5 · 2014: 5 · 2013: 4 · 2012: 3
- FWCI
- 2.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image quality assessment, spatial pattern detection, psychophysics, mean squared error, image integrity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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