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

被引：0

作者：

Saidi, Ines ^{[1
,2
]}

Zhang, Lu ^{[2
]}

Barriac, Vincent ^{[1
]}

Deforges, Olivier ^{[2
]}

机构：

[1] Orange Labs Lannion, Lannion, France

[2] INSA Rennes, CNRS UMR 6164, IETR, Rennes, France

来源：

APPLICATIONS OF DIGITAL IMAGE PROCESSING XLI | 2018年 / 10752卷

关键词：

QoE; video quality; video coding and transmission; no reference metric; Machine Learning;

D O I：

10.1117/12.2320996

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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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页数：12

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