2026-05-27T05:30:13Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/451812026-02-05T00:47:28Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Moreno-Roldán, José Miguel author Poncela-González, Javier author Luque-Nieto, Miguel Ángel author Otero-Roth, Pablo author 2017-03-23 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. 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 https://hdl.handle.net/10630/45181 10.3390/s17040664 Grabaciones en vídeo Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications