Image quality assessment based on a degradation model

• Published in: IEEE transactions on image processing Vol. 9; no. 4; pp. 636 - 650
• Main Authors: Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2000; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive noise; Additives; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer vision; robotic vision; Computers in experimental physics; Degradation; Delta modulation; Deviation; Distortion; Distortion measurement; Exact sciences and technology; Frequency measurement; Fundamental areas of phenomenology (including applications); Humans; Image contrast; Image forming and processing; Image processing; Image quality; Imaging and optical processing; Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Noise; Noise measurement; Nonlinear distortion; Optics; Pattern recognition. Digital image processing. Computational geometry; Physics; Visual; Visual system; Measuring methods; Signal-to-noise ratio; Sound quality; Experimental study; Contrast; Frequency effect; Image quality; Spatial frequency; Sensitivity; Vision; Quality control; Tagging; Interactions; Linear distortion; Non linear effect; Illuminance
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.841940

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.