RT Journal Article
T1 Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications
A1 Moreno-Roldán, José Miguel
A1 Poncela-González, Javier
A1 Luque-Nieto, Miguel Ángel
A1 Otero-Roth, Pablo
K1 Grabaciones en vídeo
AB 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.
PB MDPI
YR 2017
FD 2017-03-23
LK https://hdl.handle.net/10630/45181
UL https://hdl.handle.net/10630/45181
LA eng
NO Moreno-Roldán, José-Miguel; Luque-Nieto, Miguel-Angel; Poncela, Javier; Otero, Pablo."Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications." Sensors 17(4), 2017: Art.Id.664, pp.1-15. https://doi.org/10.3390/s17040664
NO Junta de Andalucía
NO TIC-6897
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 1 mar 2026