The Research of Image Quality Assessment Methods

In digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artifcial neural network (ANN) is proposed for...

Full description

Saved in:
Bibliographic Details
Published inPhysics procedia Vol. 25; pp. 485 - 491
Main Authors Cui, Xiaonan, Shi, Zhiyuan, Lin, Jianan, Huang, Lianfen
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2012
Subjects
Artifcial neural network
Image quality assessment
LIVE
MOS
MOS
Image quality assessment
Artifcial neural network
LIVE
Online AccessGet full text

Cover

More Information
Summary:In digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artifcial neural network (ANN) is proposed for perceptual image quality assessment. The quality prediction is based on image features such as EPSNR, blocking, and blur. Training and testing of the ANN are performed with the mean opinion scores (MOS) provided by the Laboratory for Image and Video Engineering (LIVE). It is shown that the proposed image quality assessment model is capable of predicting MOS of the five types’ image distortions.
ISSN:1875-3892
1875-3892
DOI:10.1016/j.phpro.2012.03.115