4 March 2019 Overview of full-reference video quality metrics and their performance evaluations for videoconferencing application
Lu Zhang, Ines Saidi, Shishun Tian, Vincent Barriac, Olivier Déforges
Author Affiliations +
Abstract
With the development of video services available on various devices (mobile, PC, tablet, television, etc.), the end user experiences and requirements have changed. Objective video quality assessment tools and metrics are increasingly complex and adapted to different types of service context. Compared with other reviews of quality metrics, the full-reference (FR) video quality metrics reviewed (particularly, ViS3, SSIMplus, video multimethod assessment fusion, and open perceptual video quality metric) are more recent and have not been compared with each other yet. We compare and evaluate the performance of 10 FR metrics with respect to their accuracy in predicting the subjective perceived quality for the videoconferencing application. Emphasis is given to the metrics evaluating the visual quality (from human perception). A detailed statistical study is carried out on four subjective databases: École polytechnique fédérale de Lausanne database, live mobile database, and two Orange Lab databases, with a large sample of distortion types (transmission errors, encoding bit rates, etc.).
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Lu Zhang, Ines Saidi, Shishun Tian, Vincent Barriac, and Olivier Déforges "Overview of full-reference video quality metrics and their performance evaluations for videoconferencing application," Journal of Electronic Imaging 28(2), 023001 (4 March 2019). https://doi.org/10.1117/1.JEI.28.2.023001
Received: 5 September 2018; Accepted: 8 February 2019; Published: 4 March 2019
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Video

Databases

Distortion

Data modeling

Motion models

Performance modeling

Visualization

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