Angeliki Katsenou | University of Bristol | September 29, 2020 | 10:00 (CET, 08:00 UTC)
Abstract: 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.
A 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-agnostic approach that exploits machine learning to predict the bit rate ladder with only a small number of encodes required.

Bio: 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.
Bio: Laura Toni received the M.S. and Ph.D. degrees, both in electrical engineering, from the University of Bologna, Bologna, Italy, in 2005 and 2009, respectively. In 2007, she was a Visiting Scholar at the University of California at San Diego (UCSD), San Diego, CA, USA, and since 2009, she has been a frequent visitor to the UCSD, working on media coding and streaming technologies. Between 2009 and 2011, she was with the Tele-Robotics and Application Department, Italian Institute of Technology, investigating wireless sensor networks for robotics applications. In 2012, she was a Postdoctoral Fellow at UCSD, and between 2013 and 2016, she was a Postdoctoral Fellow in the Signal Processing Laboratory (LTS4) at École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. Since July 2016, she has been a Lecturer in the Electronic and Electrical Engineering Department, University College London (UCL), U.K. Her research mainly involves interactive multimedia systems, decision-making strategies under uncertainty, large-scale signal processing, and communications. She received the UCL Future Leadership Award in 2016, the ACM Best 10% Paper Award in 2013, and the IEEE/IFIP Best Paper Award in 2012.
Bio: Lucia D’Acunto received her PhD in 2012 from Delft University of Technology, the Netherlands, with a thesis on video streaming over peer-to-peer networks. She now works as a senior research scientist at TNO, focusing on video distribution and on the impact of future internet architectures (e.g. ICN, SDN and 5G) on it. She has led and is leading various European research projects on these topics, most notably the open call projects from the European Projects TRIANGLE, 5GINFIRE and FLAME. Since 2016, Lucia is an active participant and contributor to the 3GPP SA4 group, which focusses on mobile and 5G standardization for media applications. Lucia also serves in the organizing committees of several international conferences, usually in the roles of program chair or demo chair, and in the program committees. Lucia also regularly advises European operators on network and TV technologies and contributes to 5GPPP and NEM visions on the 5G Media Vertical and pilots. Lucia has published her research in several papers and journals and holds more than 15 patent applications.

CV: Giovambattista Ianni is a full professor of Computer Science in the Department of Mathematics and Computer Science at the University of Calabria, Italy. Prof. Ianni’s current research interests include knowledge representation, reasoning, and coupling of hybrid systems. He recently focused his research interests on Artificial Intelligence in videogames with particular attention to the issue of complex and time-consuming incremental reasoning in real-time contexts. He has contributed to the DLV system and the DLVHEX system, especially dealing with the issue of dealing with, often non-symbolic, external information to knowledge bases. He has been involved in several national and international research projects and has been acknowledged with research awards such as the ICLP Test-of-time award 2018 and the Artificial Intelligence Journal Prominent Paper Award 2013.