Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using featuresthat account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. Thepurpose of this study is to analyze a number of potentially quality-relevant features in order to select the mostsuitable set of features for building the desired models. The proposed sets of features have not been used in theliterature and some of the features are used for the first time in this study. The features are employed by the leastabsolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward per-ceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression onthe reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjec-tively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RRLASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual qualitywith high accuracy, higher than that of ridge, which uses more features. The comparisons with competingworks and two full-reference metrics also verify the superiority of our models.
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