Image quality assessment is an important component in every image processing system where the last link of the chain is the human observer. This domain is of increasing interest, in particular in the context of image compression where coding scheme optimization is based on the distortion measure. Many objective image quality measures have been proposed in the literature and validated by comparing them to the Mean Opinion Score (MOS). We propose in this paper an empirical study of several indicators and show how one can improve the performances by combining them. We learn a regularized regression model and apply variable selection techniques to automatically find the most relevant indicators. Our technique enhances the state of the art results on two publicly available databases.

International audienceWe present a general formalism for Recommender Systems based on Social Network Analysis. After introducing the classical categories of recommender systems, we present our Social Filtering formalism and show that it extends association rules, classical Collaborative Filtering and Social Recommendation, while providing additional possibilities. This allows us to survey the literature and illustrate the versatility of our approach on various publicly available datasets, comparing our results with the literature

In social networks, the detection of communities has gained considerable interest because it can be used for instance for visualization, recommendation in business applications or the analysis of the spread of infectious diseases. Many methods proposed in the literature for the solution of this problem, assume that the structure of the entire network is known, which is not realistic for very large and dynamic networks. For this reason, approaches have been introduced recently to find the local community of a node. Most of these methods often fail when the starting node is at the boundary of a community. In addition, they are not able to detect overlapping communities. In this work, we propose new methods to find local communities that don't have these drawbacks. Experiences on real and computer generated social networks such as Netscience, Amazon 2006 and Lancichinetti et al.'s benchmark show that these methods perform better than the solutions with which the comparisons were performed.

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