IEEE Transactions on Image Processing, vol. 9, np. 4, pp. 636-650, Apr. 2000

Image Quality Assessment Based on a Degradation Model

Niranjan Damera-Venkata, Thomas D. Kite, Wilson S. Geisler, Brian L. Evans, and Alan C. Bovik

Department of Electrical and Computer Engineering, Engineering Science Building, The University of Texas at Austin, Austin, TX 78712-1084 USA

Matlab code for Quality Metrics

Halftoning Toolbox for Matlab - Halftoning Research at UT Austin

Image Quality Tools by sattarab extend NQM to rectangular and color images


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 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; and
  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.

Last Updated 02/23/22.