20 September 2016 Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models
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Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.
© 2016 SPIE and IS&T
Muhammad Shahid, Muhammad Shahid, Katerina Pandremmenou, Katerina Pandremmenou, Lisimachos P. Kondi, Lisimachos P. Kondi, Andreas Rossholm, Andreas Rossholm, Benny Lövström, Benny Lövström, } "Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models," Journal of Electronic Imaging 25(5), 053012 (20 September 2016). https://doi.org/10.1117/1.JEI.25.5.053012 . Submission:

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