Section: New Results

Distributed processing and robust communication

Information theory, stochastic modelling, robust detection, maximum likelihood estimation, generalized likelihood ratio test, error and erasure resilient coding and decoding, multiple description coding, Slepian-Wolf coding, Wyner-Ziv coding, information theory, MAC channels

Interactive Coding for Navigation in 3D scenes (ICON 3D)

Participants : Thomas Maugey, Aline Roumy.

In order to have performing FTV systems, the data transmission has to take into account the interactivity of the user, i.e., the viewpoint that is requested. In other words, a FTV system transmits to the visualisation support only what needs to be updated when a user changes its viewpoint angle (i.e., the new information appearing in its vision field).

In the context of the project ICON 3D funded by the GdR-Isis, we have developed new geometry prediction algorithms for surface meshes. Given a part of a mesh, the prediction algorithm is able to estimate a neighboring mesh subset corresponding to the one newly visible after user viewpoint angle change. For each mesh of a 3D model, we have generated all the predictions possible depending on the part of the model known by the decoder. Then we have characterized the prediction error.

The question of which data representation to use for Interactive Navigation has also been studied in [20]. More precisely, the navigation domain is split in small segments, each of them coded independently. This work has developed some optimal partitioning solution for different navigation scenario.

Correlation model selection for interactive video communication

Participants : Navid Mahmoudian Bidgoli, Thomas Maugey, Aline Roumy.

Interactive video communication has been recently proposed for multi-view videos. In this scheme, the server has to store the views as compactly as possible while allowing interactive navigation. Interactive navigation refers to the possibility for the user to select one view or a subset of views. To achieve this goal, the compression must be done using a model-based coding in which the correlation between the predicted view generated on the user side and the original view has to be modeled by a statistical distribution. In the context of the project Intercomm, the work published in [37] has proposed a framework for lossless fixed-length source coding to select a model among a candidate set of models that incurs the lowest extra rate cost to the system. Moreover, in cases where the depth image is available, we provide a method to estimate the correlation model.

Optimal selection of reference sensors for spatially correlated data storage

Participants : Thomas Maugey, Aline Roumy.

Highly instrumented Smart-cities, which are now common urban policies, are facing problems of management and storage of a large volume of data coming from an increasing number of sources. In the context of the project Intercom, we have proposed a data compression method by predictive coding of spatially correlated multi-source data. In a nutshell, some sensors are selected as references. They are used to predict the other sensor values, based on a Kriging prediction. We have proposed an algorithm to optimally select both the number and the position of the reference sensors among all the ones that are stored on a server and shared with a high number of users. This work has been done in collaboration with the Inria I4S project-team, IFFSTAR and the L2S.