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Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Univ Ioannina, GRC.
Univ Ioannina, GRC.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
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2016 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 25, no 5Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2016. Vol. 25, no 5
Keyword [en]
no-reference; packet loss; perceptual quality estimation; reduced-reference; video q
National Category
Engineering and Technology
URN: urn:nbn:se:bth-13256DOI: 10.1117/1.JEI.25.5.053012ISI: 000388216900023OAI: diva2:1037413
Available from: 2016-10-14 Created: 2016-10-14 Last updated: 2016-12-16Bibliographically approved

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Shahid, MuhammadAndreas, RossholmLövström, Benny
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