Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Distributed coding for interactive communication

Information theory, stochastic modeling, 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 compression scheme for interactive media

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

We propose a new interactive compression scheme for omnidirectional images and 3D model. This requires two characteristics: efficient compression of data, to lower the storage cost, and random access ability to extract part of the compressed stream requested by the user (for reducing the transmission rate). For efficient compression, data needs to be predicted by a series of references that have been pre-defined and compressed. This contrasts with the spirit of random accessibility. We propose a solution for this problem based on incremental codes implemented by rate adaptive channel codes. This scheme encodes the image while adapting to any user request and leads to an efficient coding that is flexible in extracting data depending on the available information at the decoder. Therefore, only the information which is needed to be displayed at the user’s side is transmitted during the user's request as if the request was already known at the encoder (see Fig. 4). The experimental results demonstrate that our coder obtains a better transmission rate than the state-of-the-art tile-based methods at a small cost in storage. Moreover, the transmission cost grows gradually with the size of the request and avoids a staircase effect, which shows the perfect suitability of our coder for interactive transmission. This work has led to a journal submission and several conference publications. In [25], we have proposed a new framework for evaluating the compression performance of interactive schemes. Indeed, interactive compression schemes can be characterized by tree criteria: the storage cost, the transmission rate and distortion. This contrasts with classical compression scheme, where only transmission rate and distortion are used. 3D-performance evaluation criteria are proposed. In [29], we have proposed to use the geometry to efficiently compress the 3D mesh texture. An interactive coding extension has been presented in [27].

Figure 4. A spherical image and several viewports corresponding to different user's requests.

Reference source positioning for interactive compression

Participants : Thomas Maugey, Mai Quyen Pham, Aline Roumy.

Large databases containing many HD videos or records from sensors over long time intervals, have to be efficiently compressed, to reduce their size. The compression has also to allow efficient access to random parts of the databases upon request from the users. Efficient compression is usually achieved with prediction between data points. However, this creates dependencies between the compressed representations, which is contrary to the idea of random access. Prediction methods rely in particular on reference data points, used to predict other data points, and the placement of these references balances compression efficiency and random access. Existing solutions to position the references use ad hoc methods. We study this joint problem of compression efficiency and random access. We introduce the storage cost as a measure of the compression efficiency and the transmission cost for the random access ability. We show that the reference placement problem that trades off storage with transmission cost is an integer linear programming problem, that can be solved by standard optimizer. Moreover, we show that the classical periodic placement of the references is only optimal in a very restrictive case: namely, when the encoding costs of each data point are equal and when requests of successive data points are made.