Immersive Video Technologies offers a comprehensive overview of the leading technologies that enable visual immersion, including omnidirectional (360 degrees) video, light fields and volumetric video. Following the critical components of the typical content production and delivery pipeline, the book presents acquisition, representation, coding, rendering and quality assessment approaches for each immersive video modality, reviewing current standardization efforts while also exploring new research directions.
Describes the whole content processing chain for the main immersive video modalities (omnidirectional video, light fields, and volumetric video)
Offers a common theoretical background for immersive video technologies based on the concept of plenoptic function
Presents some exemplary applications of immersive video technologies
Engineering and computer science researchers, graduate students researching and learning image and video processing, R&D engineers in industry and managers making technological decisions
Table of Contents
1. Introduction to Immersive Imaging Technologies 2. Acquisition, representation and rendering of omnidirectional videos 3. Compression and transmission of 360 video 4. Subjective and Objective Quality Assessment for Omnidirectional Video 5. Omnidirectional Video Saliency 6. Acquisition of Light Field Images and Videos 7. Representation of LF 8. Compression and transmission of Light Fields: Image/video compression standards & Learning-based coding of light fields 9. Light field processing for media applications - From practical challenges to neural rendering 10. Quality assessment for light fields 11. Volumetric Video - Acquisition, Interaction, Streaming and Rendering 12. MPEG Immersive Video 13. Point Cloud Compression 14. Coding of dynamic 3D meshes 15. Streaming of volumetric video 16. Processing of volumetric video 17. Volumetric Video Display 18. Volumetric Video Quality assessment 19. Immersive Surgery 20. Experimental production 21. Volumetric Video as a Novel Medium for Creative Storytelling 22. Video-conferencing applications (Social VR)
Giuseppe Valenzise is a CNRS researcher (chargé de recherches) at theUniversité Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes (L2S, UMR 8506), in the Telecom and Networking hub.
Giuseppe completed a master degree and a Ph.D. in Information Technology at the Politecnico di Milano, Italy, in 2007 and 2011, respectively. In 2012, he joined the French Centre National de la Recherche Scientifique (CNRS) as a permanent researcher, first at the Laboratoire Traitement et Communication de l’Information (LTCI) Telecom Paristech, and from 2016 at L2S. He got the French « Habilitation à diriger des recherches » (HDR) from Université Paris-Sud in 2019.
His research interests span different fields of image and video processing, including traditional and learning-based image and video compression, light fields and point cloud coding, image/video quality assessment, high dynamic range imaging and applications of machine learning to image and video analysis. He is co-author of more than 70 research publications and of several award-winning papers. He is the recipient of the EURASIP Early Career Award 2018.
Giuseppe serves/has served as Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Image Processing, Elsevier Signal Processing: Image communication. He is an elected member of the MMSP and IVMSP technical committees of the IEEE Signal Processing Society for the term 2018-2023, as well as a member of the Technical Area Committee on Visual Information Processing of EURASIP.
Affiliations and Expertise
Researcher, CentraleSupelec, Laboratoire des Signaux et Systemes, Universite Paris-Saclay, CNRS, France
Dr. Martin Alain received the Master's degree in electrical engineering from the Bordeaux Graduate School of Engineering (ENSEIRB-MATMECA), Bordeaux, France in 2012 and the PhD degree in signal processing and telecommunications from University of Rennes 1, Rennes, France in 2016. As a PhD student working in Technicolor and INRIA in Rennes, France, he explored novel image and video compression algorithms.
Since September 2016, he is a postdoctoral researcher in the V-SENSE project at the School of Computer Science and Statistics in Trinity College Dublin, Ireland. His research interests lie at the intersection of signal and image processing, computer vision, and computer graphics. His current topic involves light field imaging, with a focus on denoising, super-resolution, compression, scene reconstruction, and rendering.
Martin is a reviewer for the Irish Machine Vision and Image Processing conference, IEEE International Conference on Image Processing, IEEE Transactions on Image Processing, IEEE International Conference on Multimedia & Expo, IEEE International Workshop on Multimedia Signal Processing, Elsevier Signal Processing: Image Communication, IEEE Transaction on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Circuits and Systems I, and IEEE Transactions on Multimedia. He is co-organizer of the special session on Recent Advances in Immersive Imaging Technology held at EUSIPCO 2018 in Rome, ICIP 2019 in Taipei, ICME 2020 in London, and MMSP 2020 in Tampere. He co-organized the tutorial “Immersive Imaging Technologies: from Capture to Display” at ICME 2020 and ACM Multimedia 2020.
Affiliations and Expertise
Postdoctoral Researcher, V-SENSE Project, School of Computer Science and Statistics, Trinity College Dublin, Ireland
Dr. Emin Zerman is a postdoctoral research fellow in the V-SENSE project at the School of Computer Science and Statistics, Trinity College Dublin, Ireland, since February 2018. He received his Ph.D. degree (2018) in Signals and Images from Télécom ParisTech, France, and his M.Sc. degree (2013) and B.Sc. degree (2011) in Electrical and Electronics Engineering from the Middle East Technical University, Turkey. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and IEEE Signal Processing Society. He has been acting as a reviewer for several conferences and peer-reviewed journals, including Signal Processing: Image Communications, IEEE TCSVT, IEEE TIP, IEEE TMM, ACM JoCCH, IEEE MMSP, IEEE ICASSP, IEEE ICME, IEEE ICIP, European Signal Processing Conference (EUSIPCO), and International Conference on Quality of Multimedia Experience (QoMEX). He was one of the special session organizers at the ICME 2020 in London and MMSP 2020 in Tampere. He co-organized the tutorial “Immersive Imaging Technologies: from Capture to Display” at ICME 2020 and ACM Multimedia 2020. His research interests include image and video processing, high dynamic range imaging, immersive multimedia applications, human visual perception, and multimedia quality assessment.
Affiliations and Expertise
Postdoctoral Research Fellow, V-SENSE Project, School of Computer Science and Statistics, Trinity College Dublin, Ireland
Dr. Cagri Ozcinar is a senior engineer at Samsung R&D Institute UK. Before joining Samsung, he was a research fellow within the V-SENSE
project at Trinity College Dublin, Ireland. He was a post-doctoral fellow in the Multimedia group at Institut Mines-Telecom Telecom ParisTech, Paris, France. He received the M.Sc. (Hons.) and the Ph.D. degrees in electronic engineering from the University of Surrey. He is an associate editor of signal, image and video Processing (Springer). He has also been serving as a reviewer for many journals and conference proceedings, such as IEEE TIP, IEEE TCSVT, IEEE TMM, CVPR, IEEE ICASSP, IEEE ICME, IEEE ICIP, IEEE QoMEX, IEEE MMSP, EUSIPCO, BMVC, and WACV. He has been involved in organizing workshops, challenges, and special sessions in EUSIPCO, ICIP, ICME, and MMSP. He co-organized the tutorial “Immersive Imaging Technologies: from Capture to Display” at ICME 2020 and ACM Multimedia 2020. He has been involved in R&D projects that have resulted in commercialized on Samsung DTVs. His research interests include deep learning, computer vision, and immersive media.