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Synthèse de vues et reconstruction de vues à partir de vidéos compressées multi-vues et multi-sources

Abstract : Nowadays, videos are the most demanded form of multimedia. This high interest fueled a continuous evolution of display, transmission and compression technologies. Furthermore,there is also a lot of interest in finding the best way to provide a so-called immersive multimedia experience. Several solutions were investigated over the past years and the Multi-View video plus Depth format was found to provide a promising solution in combination with viewsynthesis algorithms. In this thesis we explore several topics related to view synthesis and view reconstruction.First, we explore the use of temporal correlations in combination with the traditional Depth-Image-Based-Rendering techniques and propose several approaches to tackle common problems in DIBR type algorithms which are shown to improve the quality of the synthesis. As view synthesis algorithms produce localized high distortions, we also evaluate the effectiveness of common quality evaluation metrics and propose a targeted Region-Of-Interest evaluation. Finally, we investigate the problem of multi-source video reconstruction and propose a model based framework that uses primal-dual splitting proximal convex optimization algorithms to enhance the quality and resolution of videos from multiple sources with possibly different resolutions and compression levels.
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Submitted on : Tuesday, January 18, 2022 - 6:58:08 PM
Last modification on : Thursday, January 20, 2022 - 5:16:39 PM

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  • HAL Id : tel-03533526, version 1

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Andrei Purica. Synthèse de vues et reconstruction de vues à partir de vidéos compressées multi-vues et multi-sources. Multimédia [cs.MM]. Télécom ParisTech, 2017. Français. ⟨NNT : 2017ENST0029⟩. ⟨tel-03533526⟩

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