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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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“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]
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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
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