LAUSR

Blind Image Quality Assessment (BIQA) has been an enticing research problem in image processing, during the last few decades. In spite of the introduction of several BIQA algorithms, quantifying image quality without the help of a reference image still remains an unsolved problem. We propose a method for BIQA, combining Natural Scene Statistics (NSS) feature and Probabilistic Quality representation by a CNN. A certain number of features are considered for each image. We also propose to increase the NSS feature set alongside with the same CNN architecture and compare its results accordingly. Support Vector Machine (SVM) regression is applied on these features to get a quality score for that particular image. The results obtained by applying the proposed quality score on benchmark datasets, show the effectiveness of the proposed quality metric compared to the state-of-the-art metrics.