![](/images/graphics-bg.png)
Improving the performance of no-reference image quality assessment algorithm for contrast-distorted images using natural scene statistics
Joint Authors
al-Najjar, Yusra
Der, Chen Soong
Source
Jordanian Journal of Computetrs and Information Technology
Issue
Vol. 3, Issue 3 (31 Dec. 2017), pp.201-212, 12 p.
Publisher
Princess Sumaya University for Technology
Publication Date
2017-12-31
Country of Publication
Jordan
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
This study was conducted to explore the role of two color features in improving the performance of the existing no-reference image quality assessment algorithms for contrast-distorted images (NR-IQA-CDI).
the used color features were colorfulness and naturalness of color expressed in CIELab and CIELuv color spaces.
test images used were the public benchmark databases that contain contrast-distorted images - TID2013, CID2013 and CSIQ.
experiments for the exploration were conducted in two stages: the preliminary stage and the comprehensive stage.
the results of preliminary study showed that the features of colorfulness and naturalness of color can improve the prediction of human opinion score which relies mainly on the feature of brightness-only contrast.
the results inspired to more comprehensive study where the natural scene statistics (NSS) of these two features were estimated by modelling the probability distribution function (pdf) of 16,873 test images from a public database called sun2012.
the results based on k-fold cross validation with k ranging from 2 to 10 showed that the performance of NR-IQA-CDI can be improved by adding the NSS of these features.
American Psychological Association (APA)
al-Najjar, Yusra& Der, Chen Soong. 2017. Improving the performance of no-reference image quality assessment algorithm for contrast-distorted images using natural scene statistics. Jordanian Journal of Computetrs and Information Technology،Vol. 3, no. 3, pp.201-212.
https://search.emarefa.net/detail/BIM-1415322
Modern Language Association (MLA)
al-Najjar, Yusra& Der, Chen Soong. Improving the performance of no-reference image quality assessment algorithm for contrast-distorted images using natural scene statistics. Jordanian Journal of Computetrs and Information Technology Vol. 3, no. 3 (Dec. 2017), pp.201-212.
https://search.emarefa.net/detail/BIM-1415322
American Medical Association (AMA)
al-Najjar, Yusra& Der, Chen Soong. Improving the performance of no-reference image quality assessment algorithm for contrast-distorted images using natural scene statistics. Jordanian Journal of Computetrs and Information Technology. 2017. Vol. 3, no. 3, pp.201-212.
https://search.emarefa.net/detail/BIM-1415322
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 210-211
Record ID
BIM-1415322