From Data Analytics, on data collected from connected devices (gateways and sensors) that will help predicting user behaviors, to Virtualization, that will change traditional approach to delivery of applications to the Home, find out how Technicolor envision the future of people and devices in the connected home.

Network Evolution

Anticipating technology evolution in cooperation with our business units we conduct applied research activities linked with networking and computer science. Our technologies relate to packet processing, protocols, architectures and applications for network infrastructures as media streaming (e.g. HTTP adaptive streaming), channel bonding or yet network virtualization.

Future Edge Network

The goal of the Future Edge Network technology area is to anticipate the complexity related to heterogeneous networks and forthcoming 5G, in the home and near to the home. We aim at providing innovative technologies around smart antennas, advanced protocols and devices, devices to devices, small cells as well as taking benefits of virtualized networks.  The target is to shape an unreached experience in the home over next generation of networks.

Immersive Devices

Our goal is to improve immersive experience on heterogeneous devices such as tablets, phones, VR cardboards and set top boxes.

We are covering several research topics such as natural interactions, gesture control and body tracking, real-time reconstruction, streaming and rendering of live real environments, autostereoscopic rendering.

We apply those technologies to related applications like multi-user 360 VR consumption, telepresence or live sport events replay in VR.


Data Analytics

The Data Analytics Technical Area (DA TA) focuses on data mining & machine learning (ML) technologies to increase the user experience at home by leveraging data collected through our residential gateways and set-top boxes. To understand system behavior and to create innovative solutions, we rely on expertise in network and sensor data. Our research focuses on failure prediction, pattern matching, context inference and semi-supervised learning. We also cover aspects of data collection and storage at scale, optimization of distributed deep learning algorithms and event/stream processing.