Agrandissement adapté des GOP (Group Of Pictures) en vidéo

Cet article traite de l'agrandissement de séquences vidéo dans le but de diminuer les besoins en capacités de stockage et en débits de transmission. Nous définissons un procédé permettant d'agrandir, en résolution spatiale, les images d'une séquence qui peut ensuite être stockée sous forme de "petit format". Dans un premier temps, nous traitons l'agrandissement d'images par induction, une méthode de régularisation développée pour les images fixes que nous étendons à un facteur agrandissement quelconque. Ensuite, nous expliquons comment les vecteurs de mouvement et les images d'erreurs obtenus par un algorithme de Block-matching sont utilisés avec l'induction pour réaliser un agrandissement de séquences vidéo de qualité

Consistent Image Decoding from Multiple Lossy Versions

With the recent development of tools for data sharing in social networks and peer to peer networks, the same information is often stored in different nodes. Peer-to-peer protocols usually allow one user to collect portions of the same file from different nodes in the network, substantially improving the rate at which data are received by the end user. In some cases, however, the same multimedia document is available in different lossy versions on the network nodes. In such situations, one may be interested in collecting all available versions of the same document and jointly decoding them to obtain a better reconstruction of the original. In this paper we study some methods to jointly decode different versions of the same image. We compare different uses of the method of Projections Onto Convex Sets (POCS) with some Convex Optimization techniques in order to reconstruct an image for which JPEG and JPEG2000 lossy versions are available

On the performance of algorithms for the minimization of ℓ1\ell_1-penalized functionals

The problem of assessing the performance of algorithms used for the minimization of an $\ell_1$-penalized least-squares functional, for a range of penalty parameters, is investigated. A criterion that uses the idea of `approximation isochrones' is introduced. Five different iterative minimization algorithms are tested and compared, as well as two warm-start strategies. Both well-conditioned and ill-conditioned problems are used in the comparison, and the contrast between these two categories is highlighted.Comment: 18 pages, 10 figures; v3: expanded version with an additional synthetic test problem