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ON THE IMPROVEMENT OF NO-REFERENCE MEAN OPINION SCORE ESTIMATION ACCURACY BY FOLLOWING A FRAME-LEVEL REGRESSION APPROACH
Univ Ioannina, Dept Comp Sci & Engn, GR-45110 Ioannina, Greece..
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Univ Ioannina, Dept Comp Sci & Engn, GR-45110 Ioannina, Greece..
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2015 (English)In: 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2015, 1850-1854 p.Conference paper (Refereed)Text
Abstract [en]

In order to estimate subjective video quality, we usually deal with a large number of features and a small sample set. Applying regression on complex datasets may lead to imprecise solutions due to possibly irrelevant or noisy features as well as the effect of overfitting. In this work, we propose a No-Reference (NR) method for the estimation of the quality of videos that are impaired by both compression artifacts and packet losses. Particularly, in an effort to establish a robust regression model that generalizes well to unknown data and to increase Mean Opinion Score (MOS) estimation accuracy, we propose a frame-level MOS estimation approach, where the MOS estimate of a sequence is obtained by averaging the perframe MOS estimates, instead of performing regression directly at the sequence-level. Since it is impractical to obtain the actual perframe MOS values through subjective experiments, we propose an objective metric able to do this task. Thus, our proposed NR method has the dual benefit of offering improved sequence-level MOS estimation accuracy, while giving an indication of the relative quality of each individual video frame.

Place, publisher, year, edition, pages
2015. 1850-1854 p.
Series
IEEE International Conference on Image Processing ICIP, ISSN 1522-4880
Keyword [en]
Estimation accuracy, frame-level quality estimation, MOS, objective metric, sequence-level quality estimation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-11930ISI: 000371977801194ISBN: 978-1-4799-8339-1OAI: oai:DiVA.org:bth-11930DiVA: diva2:931677
Conference
IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA
Available from: 2016-05-30 Created: 2016-05-30 Last updated: 2016-06-03Bibliographically approved

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