In this paper, we study the sampling and the distributed compression of the data acquired by a camera sensor network. The effective design of these sampling and compression schemes requires, however, the understanding of the structure of the acquired data. To this end, we show that the a priori knowledge of the configuration of the camera sensor network can lead to an effective estimation of such structure and to the design of effective distributed compression algorithms. For idealized scenarios, we derive the fundamental performance bounds of a camera sensor network and clarify the connection between sampling and distributed compression. We then present a distributed compression algorithm that takes advantage of the structure of the data and that outperforms independent compression algorithms on real multiview images.

In this paper, we propose a distributed compression approach for multi-view images, where each camera efficiently encodes its visual information locally without requiring any collaboration with the other cameras. Such a compression scheme can be necessary for camera sensor networks, where each camera has limited power and communication resources and can only transmit data to a central base station. The correlation in the multi-view data acquired by a dense multi-camera system can be extremely large and should therefore be exploited at each encoder in order to reduce the amount of data transmitted to the receiver. Our distributed source coding approach is based on a quadtree decomposition method and uses some geometrical information about the scene and the position of the cameras to estimate this multi-view correlation. We assume that the different views can be modelled as 2D piecewise polynomial functions with ID linear boundaries and show how our approach applies in this context. Our simulation results show that our approach outperforms independent encoding of real multi-view images.

We study the problem of distributed sampling and compression in sensor networks when the sensors are digital cameras that acquire a 3-D visual scene of interest from different viewing positions. We assume that sensors cannot communicate among themselves, but can process their acquired data and transmit it to a common central receiver. The main task of the receiver is then to reconstruct the best possible estimation of the original scene and the natural issue, in this context, is to understand the interplay in the reconstruction between sampling and distributed compression. In this paper, we show that if the observed scene belongs to the class of signals that can be represented with a finite number of parameters, we can determine the minimum number of sensors that allows perfect reconstruction of the scene. Then, we present a practical distributed coding approach that leads to a rate-distortion behaviour at the decoder that is independent of the number of sensors, when this number increases beyond the critical sampling. In other words, we show that the distortion at the decoder does not depend on the number of sensors used, but only on the total number of bits that can be transmitted from the sensors to the receiver

In this paper, we propose a practical coding approach for the problem of distributed compression of multi-view images. Our coding technique is based on a tree structured compression algorithm that guarantees an optimal rate-distortion behaviour for piecewise polynomial signals. We model the different views using a piecewise polynomial function whose singularity positions are shifted from one view to the others according to the constraints imposed by the structure of the plenoptic function. We show that, starting from the optimal tree decompositions of the different views, only partial information from each tree is necessary at the decoder in order to reconstruct all the different approximations. We first present our approach in the more intuitive 1D case and show that it can be used with arbitrary bit-rate allocation. Then, we propose a construction for the case of TV different views that satisfies a certain bit conservation principle. Finally, we show how our approach can be extended to the 2D case.

In this paper, we consider the problem of distributed compression in camera sensor networks. Due to the spatial proximity of the different cameras, acquired images can be highly dependent. The correlation in the visual information retrieved is related to the structure of the plenoptic function and can be estimated using geometrical information such as the position of the cameras and some bounds on the location of the objects. We propose a distributed compression scheme that takes advantage of this geometrical information in order to reduce the overall transmission rate from the sensors to a common central receiver. This new approach allows for a flexible repartition of the transmission bit-rates amongst the encoders and is optimal in many cases. Moreover, we show that our coding scheme can be made resilient to a fixed number of occlusions and that perfect reconstruction and interpolation are possible at the receiver.

We address the problem of distributed compression in camera sensor networks. Our approach uses some geometrical information in order to estimate the correlation in the visual data. This correlation, which is related to the structure of the plenoptic function, can then be used to reduce the overall transmission rate from the sensors to a common central receiver. Our approach allows for a flexible allocation of the bit-rates amongst the encoders and can be made resilient to a fixed number of occlusions. Finally, we show that our distributed coding approach can be extended to general binary sources. The technique we propose uses linear channel codes and can achieve any point of the Slepian-Wolf achievable rate region.

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