By Topic

No-reference quality assessment of JPEG images by using CBP neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Gastaldo, P. ; Dept. of Biophys. & Electron. Eng., Genoa Univ., Genova, Italy ; Zunino, R.

Reliable methods for measuring the perceived image quality are needed to evaluate visual artifacts brought about by digital compression algorithms such as JPEG. This paper presents an objective quality-assessment method based on a circular back-propagation (CBP) neural structure: the network is trained to predict quality ratings, as scored by human assessors, from numerical features that characterize images. As such, the method aims at reproducing perceived image quality, rather than at defining a comprehensive model of the human visual system. The neural model allows one to decouple the task of feature selection from the mapping of these features into a quality score. Experimental results on a public database of test images confirm the effectiveness of the approach.

Published in:

Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on  (Volume:5 )

Date of Conference:

23-26 May 2004