Machine Learning approach for global no-reference video quality model generation

I. Saidi 1 L. Zhang 1 V. Barriac 2 O. Deforges 1
1 IETR - Institut d'Electronique et de Télécommunications de Rennes
2 Orange Labs [Lannion]
Abstract : Offering the best Quality of Experience (QoE) is the challenge of all the video conference service providers. In this context it is essential to identify the representative metrics to monitor the video quality. In this paper, we present Machine Learning techniques for modeling the dependencies of different video impairments to the global video quality perception using subjective quality feedback. We investigate the possibility of combining no-reference single artifact metrics in a global video quality assessment model. The obtained model has an accuracy of 63% of correct prediction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Keywords : Machine Learning no reference metric QoE video coding and transmission video quality
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Conference papers
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01904636
Contributor : Laurent Jonchère <>
Submitted on : Thursday, October 25, 2018 - 11:01:12 AM
Last modification on : Friday, November 16, 2018 - 1:25:50 AM

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UNIV-RENNES1 | IETR | UR1-UFR-ISTIC | IETR-VAADER | UNIV-NANTES | UNIV-RENNES | INSA-GROUPE

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I. Saidi, L. Zhang, V. Barriac, O. Deforges. Machine Learning approach for global no-reference video quality model generation. Applications of Digital Image Processing XLI 2018, Aug 2018, San Diego, United States. pp.1075212, ⟨10.1117/12.2320996⟩. ⟨hal-01904636⟩

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