Share Email Print

Proceedings Paper

Univariant assessment of the visual quality of images
Author(s): Mathieu Jung; Dominique Leger
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In order to evaluate the visual quality of images, most methods compare a degraded image to a perfect reference. We propose an original univariant (i.e. without reference) method based on the use of artificial neural networks. The principle is first to use a neural network to learn the quality of images taken from a pool of known examples, then use it to assess the quality of unknown images. The considered defects are compression artefacts, ringing or local singularities. To simplify, only images with defects that are not mixed with each other were first used. The method follows four steps. Observers are first required to mark degraded images to establish a pool of examples. Then, a characterization of the defect is extracted mathematically from the image. Then, the neural network learns how to establish a relation between the mathematical characterization of the defect and the visual mark. Finally, it can be used to assess the visual quality of an unknown image from the mathematical characterization of its defects. Two illustrative examples are presented: the assessment of the quality of JPEG compressed images and the detection of local defects.

Paper Details

Date Published: 2 June 2000
PDF: 10 pages
Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); doi: 10.1117/12.387195
Show Author Affiliations
Mathieu Jung, ONERA Ctr. de Toulouse (France)
Dominique Leger, ONERA Ctr. de Toulouse (France)

Published in SPIE Proceedings Vol. 3959:
Human Vision and Electronic Imaging V
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

© SPIE. Terms of Use
Back to Top