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Open AccessArticle

Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications

Department of Communication Engineering, University of Málaga, 29071 Málaga, Spain
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Author to whom correspondence should be addressed.
Academic Editor: José-Fernán Martínez
Sensors 2017, 17(4), 664; https://doi.org/10.3390/s17040664
Received: 30 January 2017 / Revised: 15 March 2017 / Accepted: 21 March 2017 / Published: 23 March 2017
Video services are meant to be a fundamental tool in the development of oceanic research. The current technology for underwater networks (UWNs) imposes strong constraints in the transmission capacity since only a severely limited bitrate is available. However, previous studies have shown that the quality of experience (QoE) is enough for ocean scientists to consider the service useful, although the perceived quality can change significantly for small ranges of variation of video parameters. In this context, objective video quality assessment (VQA) methods become essential in network planning and real time quality adaptation fields. This paper presents two specialized models for objective VQA, designed to match the special requirements of UWNs. The models are built upon machine learning techniques and trained with actual user data gathered from subjective tests. Our performance analysis shows how both of them can successfully estimate quality as a mean opinion score (MOS) value and, for the second model, even compute a distribution function for user scores. View Full-Text
Keywords: objective video quality assessment; machine learning; MOS; VQA; QoE objective video quality assessment; machine learning; MOS; VQA; QoE
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MDPI and ACS Style

Moreno-Roldán, J.-M.; Luque-Nieto, M.-Á.; Poncela, J.; Otero, P. Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications. Sensors 2017, 17, 664.

AMA Style

Moreno-Roldán J-M, Luque-Nieto M-Á, Poncela J, Otero P. Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications. Sensors. 2017; 17(4):664.

Chicago/Turabian Style

Moreno-Roldán, José-Miguel; Luque-Nieto, Miguel-Ángel; Poncela, Javier; Otero, Pablo. 2017. "Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications" Sensors 17, no. 4: 664.

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