Special Section on Ultrawide Context- and Content-Aware Imaging Part II

No-reference video quality measurement: added value of machine learning

[+] Author Affiliations
Decebal Constantin Mocanu

Eindhoven University of Technology, Department of Electrical Engineering, FLX 9.104, P.O. Box 513, 5600 MB, Eindhoven, the Netherlands

Jeevan Pokhrel

Montimage, 39 rue Bobillot, Paris 75013, France

Juan Pablo Garella

Universidad de la República, Facultad de Ingeniería, Julio Herrera y Reissig 565, Montevideo 11300, Uruguay

Janne Seppänen

VTT Technical Research Centre of Finland Ltd., Network Performance Team, Kaitoväylä 1, Oulu 90590, Finland

Eirini Liotou

National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens 15784, Greece

Manish Narwaria

Dhirubhai Ambani Institute of Information and Communication Technology, Near Indroda Circle, Gandhinagar, Gujarat 382007, India

J. Electron. Imaging. 24(6), 061208 (Dec 29, 2015). doi:10.1117/1.JEI.24.6.061208
History: Received May 15, 2015; Accepted December 1, 2015
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Abstract.  Video quality measurement is an important component in the end-to-end video delivery chain. Video quality is, however, subjective, and thus, there will always be interobserver differences in the subjective opinion about the visual quality of the same video. Despite this, most existing works on objective quality measurement typically focus only on predicting a single score and evaluate their prediction accuracies based on how close it is to the mean opinion scores (or similar average based ratings). Clearly, such an approach ignores the underlying diversities in the subjective scoring process and, as a result, does not allow further analysis on how reliable the objective prediction is in terms of subjective variability. Consequently, the aim of this paper is to analyze this issue and present a machine-learning based solution to address it. We demonstrate the utility of our ideas by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application. We conducted meaningful verification studies on different video content (including video clips recorded from real DTT broadcast transmissions) in order to verify the performance of the proposed solution.

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Citation

Decebal Constantin Mocanu ; Jeevan Pokhrel ; Juan Pablo Garella ; Janne Seppänen ; Eirini Liotou, et al.
"No-reference video quality measurement: added value of machine learning", J. Electron. Imaging. 24(6), 061208 (Dec 29, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.061208


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