Séminaire ICI : Aline Roumy

Titre du séminaire et oratrice

Source coding under massive random access: theory and applications.

Aline Roumy (INRIA, Rennes)

Date et lieu

Vendredi 16 avril 2018, 11h

ENSEA, salle 384


In this presentation we will introduce a novel source coding problem allowing massive random access to large databases. Indeed, we consider a database that is so large that, to be stored on a single server, the data have to be compressed efficiently, meaning that the redundancy/correlation between the data have to be exploited. The dataset is then stored on a server and made available to users that may want to access only a subset of the data. Such a request for a subset of the data is indeed random, since the choice of the subset is user-dependent. Finally, massive requests are made, meaning that, upon request, the server can only perform low complexity operations (such as bit extraction but no decompression/compression).
After describing the problem, information theoretical bounds of the source coding problem will be derived. Then two applications will be presented: Free-viewpoint Television (FTV) and massive requests to a database collecting data from a large-scale sensor network (such as Smart Cities).


Aline Roumy received the Engineering degree from Ecole Nationale Superieure de l'Electronique et de ses Applications (ENSEA), France in 1996, the Master degree in 1997 and the Ph.D. degree in 2000 from the University of Cergy-Pontoise, France. During 2000-2001, she was a research associate at Princeton University, Princeton, NJ. On November 2001, she joined INRIA, Rennes, France as a research scientist. She has held visiting positions at Eurecom and Berkeley University. She serves as an Associate Editor for the Annals of telecommunications. Her current research and study interests include the area of statistical signal and image processing, coding theory and information theory. She is currently leading a project entitled Interactive Communication (InterCom) on Massive random access to subsets of compressed correlated data, and supported by the French Cominlabs excellence laboratory.