BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Informatik Austria - ECPv5.16.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Informatik Austria
X-ORIGINAL-URL:https://www.informatikaustria.at
X-WR-CALDESC:Veranstaltungen für Informatik Austria
BEGIN:VTIMEZONE
TZID:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200929T100000
DTEND;TZID=Europe/Berlin:20200929T120000
DTSTAMP:20261006T205749
CREATED:20200916T075345Z
LAST-MODIFIED:20200916T075345Z
UID:3639-1601373600-1601380800@www.informatikaustria.at
SUMMARY:
DESCRIPTION:Content-gnostic Bitrate Ladder Prediction for Adaptive Video Streaming\n\nAngeliki Katsenou | University of Bristol | September 29\, 2020 | 10:00 (CET\, 08:00 UTC) | online (registration here) \nAbstract: Cisco reported in the past reports that the video data share was expected to reach 80% by the year 2023. However\, due to the pandemic and recently imposed a remote work lifestyle\, this figure is expected to increase even more. Except for the on-demand and conferencing services\, the number of users that are generating\, storing\, and sharing their content usually through either social media platforms or video sharing platforms is increasing. Meanwhile from the video coding perspective\, as video technologies evolve towards improved compression performance\, their complexity inversely increases. \nA challenge that many video service providers face is the heterogeneity of networks and display devices for streaming\, as well as dealing with a wide variety of content with different encoding performance. In the past\, a fixed bit rate ladder solution based on a „fitting all“ approach has been employed. However\, such a content-tailored solution is highly demanding; the computational and financial cost of constructing the convex hull per video by encoding at all resolutions and quantization levels is huge. In this talk\, we present a content-gnostic approach that exploits machine learning to predict the bit rate ladder with only a small number of encodes required. \nBio: Angeliki Katsenou is a Leverhulme Early Career Fellow and is with the Visual Information Lab at the University of Bristol since 2015. She obtained her Ph.D. degree from the Department of Computer Science and Engineering\, University of Ioannina\, Greece (2014). She received her Diploma in Electrical and Computer Engineering and an M.Sc. degree in Signal and Image Processing from the University of Patras\, Greece. She has experience in several FP7 EC-funded and EPSRC projects\, such as MSCA-ITN PROVISION and EPSRC Platform Grant EP/M000885/1. Her research interests include perceptual video analysis\, video compression\, image/video quality\, and resource allocation for video communication systems. She has also been involved with conference organization activities and is currently one of the Technical Program Co-Chairs for Picture Coding Symposium (PCS) 2021\, Bristol\, UK.
URL:https://www.informatikaustria.at/event/3639/
CATEGORIES:AAU Klagenfurt
END:VEVENT
END:VCALENDAR