Share Email Print

Journal of Electronic Imaging

Efficient neural-network-based no-reference approach to an overall quality metric for JPEG and JPEG2000 compressed images
Author(s): Hantao Liu; Judith A. Redi; Hani Alers; Rodolfo Zunino; Ingrid Heynderickx
Format Member Price Non-Member Price
PDF $20.00 $25.00
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

Reliably assessing overall quality of JPEG/JPEG2000 coded images without having the original image as a reference is still challenging, mainly due to our limited understanding of how humans combine the various perceived artifacts to an overall quality judgment. A known approach to avoid the explicit simulation of human assessment of overall quality is the use of a neural network. Neural network approaches usually start by selecting active features from a set of generic image characteristics, a process that is, to some extent, rather ad hoc and computationally extensive. This paper shows that the complexity of the feature selection procedure can be considerably reduced by using dedicated features that describe a given artifact. The adaptive neural network is then used to learn the highly nonlinear relationship between the features describing an artifact and the overall quality rating. Experimental results show that the simplified feature selection procedure, in combination with the neural network, indeed are able to accurately predict perceived image quality of JPEG/JPEG2000 coded images.

Paper Details

Date Published: 1 October 2011
PDF: 16 pages
J. Electron. Imag. 20(4) 043007 doi: 10.1117/1.3664181
Published in: Journal of Electronic Imaging Volume 20, Issue 4
Show Author Affiliations
Hantao Liu, Technische Univ. Delft (Netherlands)
Judith A. Redi, Technische Univ. Delft (Netherlands)
Hani Alers, Technische Univ. Delft (Netherlands)
Rodolfo Zunino, Univ. degli Studi di Genova (Italy)
Ingrid Heynderickx, Technische Univ. Delft (Netherlands)

© SPIE. Terms of Use
Back to Top