Results 201 to 210 of about 81,008 (252)

Effect of low-dose, high-frequency advanced life support training versus annual full-day training on simulation-based resuscitation performance: a randomized controlled trial. [PDF]

BMC Med Educ
Moll L, Riessen R, Dahlmann P, Häske D.
europepmc   +1 more source

Evaluation of the usability of YouTube videos as educational materials in improving patients' metered dose inhaler use skills: A cross-sectional study. [PDF]

Medicine (Baltimore)
Arslan C, Yildiz H.
europepmc   +1 more source

Quality and reliability of neonatal jaundice short videos on TikTok and Bilibili. [PDF]

BMC Pediatr
Liu Z, Yang X, Wang H.
europepmc   +1 more source

Tetanus Health Information on YouTube and TikTok: Cross-Sectional Analysis of Quality, Reliability, and Public Health Implications. [PDF]

JMIR Infodemiology
Huang R, Shen Y, Zou G, Zhou J, Deng X, Li Q, Chen X.   +6 more
europepmc   +1 more source

A Lightweight Temporal Convolutional Network for Contactless SPPB-Aligned Functional Fall-Risk Stratification in Older Adults Using Monocular RGB Video. [PDF]

Sensors (Basel)
Lin KC, Wai RJ, Chang Chien HY.
europepmc   +1 more source

Determination of Optimal Magill Forceps Hand Position and Laryngoscope Type to Remove a Simulated Foreign Body Airway Obstruction. [PDF]

West J Emerg Med
Berkenbush M, Thomas C, Mysh M, Rutledge J, Dwyer R, Kansky S.   +5 more
europepmc   +1 more source

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No-reference screen content video quality assessment

Displays, 2021
Abstract How to effectively and accurately measure the degradation of media content is an important research topic in the field of image or video processing. Application scenarios such as online meetings, distance learning, and live game streaming make screen content video become a hot spot in Video Quality Assessment (VQA) research.
Teng Li 0006, Xiongkuo Min, Wenhan Zhu, Yiling Xu, Wenjun Zhang 0001   +4 more
openaire   +1 more source

COME for No-Reference Video Quality Assessment

2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018
Nowadays, the issue of objective Video Quality Assessment (VQA) has been extensively studied. In this paper, we present an effective general-purpose VQA method named COnvolutional neural network and Multi-regression based Evaluation (COME). It requires no referred lossless video and is universal for non-specific types of distortion.
Chunfeng Wang, Li Su 0003, Weigang Zhang
openaire   +2 more sources