IEEE Transactions on Image Processing,
vol. 9, np. 4, pp. 636-650, Apr. 2000
Image Quality Assessment Based on a Degradation Model
Wilson S. Geisler,
Brian L. Evans, and
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
Toolbox for Matlab -
at UT Austin
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
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.
- variation in contrast sensitivity with distance, image dimensions,
and spatial frequency;
- variation in the local luminance mean;
- contrast interaction between spatial frequencies; and
- contrast masking effects.
Last Updated 10/24/04.