No-reference quality assessment of highly compressed video sequences

In this paper we present a no-reference quality assessment algorithm for highly compressed H. 264 videos. By analyzing the spatio-temporal artifacts and their effect on perceived visual quality we produced an array of viable predictors of quality. Using a feature selection method a small sub-set of features, each applied to a specific artefact-domain, was selected for optimal quality estimation. The features are mapped into video quality scores using a simple linear function, which is computationally efficient and minimizes the effect learning of content. The resulting algorithm is evaluated on content independent sets of highly compressed H. 264 video sequences from the LIVE video database where it shows high correlation with the subjective scores.