Sparse representation based classifier to assess video quality

This paper describes a sparse representation based approach to learn a classifier for assessing the video quality without a reference. First we calculate the natural scene statistics (NSS) based spatial features of each frame/image and then learn a dictionary by K-SVD algorithm from NSS features of correct frames. In this work we identified the fact that correct frame can be represented precisely in terms of dictionary atoms but while representing a distorted frame, the error drastically increases with increase in distortion thus we can easily classify the frames as correct and distorted based on error score calculated by sparse representation framework. This framework has been validated on two datasets and we observe improved accuracies as compared to state-of-art algorithms.