IMPROVING THE PERFORMANCE OF NO-REFERENCE IMAGE QUALITY ASSESSMENT ALGORITHM FOR CONTRAST-DISTORTED IMAGES USING NATURAL SCENE STATISTICS

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.