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

Ines Saidi; Lu Zhang; Vincent Barriac; Olivier Deforges

doi:10.1117/12.2320996

17 September 2018 Machine Learning approach for global no-reference video quality model generation

Ines Saidi, Lu Zhang, Vincent Barriac, Olivier Deforges

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 1075212 (2018) https://doi.org/10.1117/12.2320996
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

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

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Citation Download Citation

Ines Saidi, Lu Zhang, Vincent Barriac, and Olivier Deforges "Machine Learning approach for global no-reference video quality model generation", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 1075212 (17 September 2018); https://doi.org/10.1117/12.2320996

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