A framework for realistic 3D tele-immersion

Meeting, socializing and conversing online with a group of people using teleconferencing systems is still quite differ- ent from the experience of meeting face to face. We are abruptly aware that we are online and that the people we are engaging with are not in close proximity. Analogous to how talking on the telephone does not replicate the experi- ence of talking in person. Several causes for these differences have been identified and we propose inspiring and innova- tive solutions to these hurdles in attempt to provide a more realistic, believable and engaging online conversational expe- rience. We present the distributed and scalable framework REVERIE that provides a balanced mix of these solutions. Applications build on top of the REVERIE framework will be able to provide interactive, immersive, photo-realistic ex- periences to a multitude of users that for them will feel much more similar to having face to face meetings than the expe- rience offered by conventional teleconferencing systems

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Fusion of Global and Local Motion Estimation Using Foreground Objects for Distributed Video Coding

International audienceThe side information in distributed video coding is estimated using the available decoded frames, and exploited for the decoding and reconstruction of other frames. The quality of the side information has a strong impact on the performance of distributed video coding. Here we propose a new approach that combines both global and local side information to improve coding performance. Since the background pixels in a frame are assigned to global estimation and the foreground objects to local estimation, one needs to estimate foreground objects in the side information using the backward and forward foreground objects, The background pixels are directly taken from the global side information. Specifically, elastic curves and local motion compensation are used to generate the foreground objects masks in the side information. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.39 dB for a GOP size of 2, and up to 4.73 dB for larger GOP sizes, with respect to the reference DISCOVER codec. Index Terms A. ABOU-ELAILAH, F. DUFAUX, M. CAGNAZZO, and B. PESQUET-POPESCU are with the Signal and Image Processin