IMAGE QUALITY ASSESSMENT and CONFORMANCE TESTING

Univariant assessment of the quality of images

[+] Author Affiliations
Mathieu Jung

Thales Information Systems, 105 Avenue du Ge´ne´ral Eisenhower, 31037 Toulouse Cedex, France E-mail: mathieu.jung@thales-is.com

Dominique Le´ger

ONERA Centre de Toulouse, De´partement Optique The´orique et Applique´e, 2 Avenue Edouard Belin, BP 4025, 31055 Toulouse Cedex 4, France

Marc Gazalet

Universite´ de Valenciennes et du Hainaut Cambre´sis, De´partement Opto-Acousto-Electronique, Le Mont Houy, 59313 Valenciennes Cedex 9, France

J. Electron. Imaging. 11(3), 354-364 (Jul 01, 2002). doi:10.1117/1.1482096
History: Received Feb. 23, 2000; Revised Jul. 21, 2000; Accepted Feb. 1, 2002; Online July 25, 2002
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To evaluate the quality of images, most methods compare a degraded image to a perfect reference. Nevertheless in many cases, a reference does not exist. We propose an original univariant (i.e., without a reference) method based on the use of artificial neural networks. The principle behind it is to first teach a neural network to assess image quality using images taken from a pool of known examples, then use it to assess the quality of unknown images. The defects considered are compression artifacts, ringing, local singularities, etc. To simplify, only images with defects that are not mixed with one another were first used. Two illustrative examples are presented: assessment of the quality of JPEG compressed images and detection of local defects. The quality of the images is assessed without a reference and with error less than 6%–7% compared to the bivariant method that was learned. Our method can even be used to model some very simple visual comportment. © 2002 SPIE and IS&T.

© 2002 SPIE and IS&T

Citation

Mathieu Jung ; Dominique Le´ger and Marc Gazalet
"Univariant assessment of the quality of images", J. Electron. Imaging. 11(3), 354-364 (Jul 01, 2002). ; http://dx.doi.org/10.1117/1.1482096


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