The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Image quality assessment based on a degradation model
Abstract
We model a degraded image as an original image that has been subject to linear frequency distortion and additive noise injection. Since the psychovisual effects of frequency distortion and noise injection are independent, we decouple these two sources of degradation and measure their effect on the human visual system. We develop a distortion measure (DM) of the effect of frequency distortion, and a noise quality measure (NQM) of the effect of additive noise. The NQM, which is based on Peli's (1990) contrast pyramid, takes into account the following: 1) variation in contrast sensitivity with distance, image dimensions, and spatial frequency; 2) variation in the local luminance mean; 3) contrast interaction between spatial frequencies; 4) contrast masking effects. For additive noise, we demonstrate that the nonlinear NQM is a better measure of visual quality than peak signal-to noise ratio (PSNR) and linear quality measures. We compute the DM in three steps. First, we find the frequency distortion in the degraded image. Second, we compute the deviation of this frequency distortion from an allpass response of unity gain (no distortion). Finally, we weight the deviation by a model of the frequency response of the human visual system and integrate over the visible frequencies. We demonstrate how to decouple distortion and additive noise degradation in a practical image restoration system.
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Cites
The effects of a visual fidelity criterion of the encoding of images
Mannos, J., & Sakrison, D. (1974) · IEEE Transactions on Information Theory · doi:10.1109/tit.1974.1055250 · Cited by 1,063 · cited 2×
“Fig. 3 shows a bandpass CSF [ 14 ], a lowpass CSF [3], [4], and a CTF.”
BackgroundMethodTeo, P. C., & Heeger, D. J. (2002) · Proceedings of 1st International Conference on Image Processing · doi:10.1109/icip.1994.413502 · Cited by 471
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Wang, Z., & Bovik, A. C. (2006) · Synthesis Lectures on Image, Video, and Multimedia Processing · doi:10.1007/978-3-031-02238-8 · Cited by 967
Self-citationObjective video quality assessment
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Metadata
- Authors
- Damera-Venkata, N., Kite, T. D., Geisler, W. S., Evans, B. L., & Bovik, A. C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2000-04
- Volume
- 9
- Issue
- 4
- Pages
- 636-650
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2144468361
- Cited by
- 806
- References
- 2
- FWCI
- 14.47
- Type
- journal article
- Language
- en
- Open access
- closed
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