Distributed Sampling and Compression of Scenes with Finite Rate of Innovation in Camera Sensor Networks

Abstract

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.

Sampling Schemes for Multidimensional Signals With Finite Rate of Innovation

“…In fact, the proposed schemes have been promisingly explored for super-resolution algorithms [5] and distributed DRAFT November 27,2006 compression [6]. Finally, the use of the corner reconstruction algorithm for super-resolution photogrammetry is under investigation.…”

“…α, β ∈ {0, 1} in (5). Therefore, from (6), an algebraic sum of shifted kernels is constant and equals to unity (see Figure 2 Therefore, for a given Dirac (assuming T x = T y = 1), it follows that…”

“…circles, ellipses, cardioids), 2-D polynomials with polygonal boundaries, and n-dimensional Diracs and convex polytopes. This research has been promisingly explored in super-resolution algorithms [5] and distributed compression [6], and might find its applications in photogrammetry, computer graphics, and machine vision. …”

Compression of Short Text on Embedded Systems

“…Most of the sensor data compression techniques exploit statistical correlations between the typically larger data of multiple sensors as they all observe the same phenomenon, see for instance [9], [10]. The method proposed in this paper can help sensors to efficiently exchange sensor specific signaling data.…”

RIDA: A Robust Information-Driven Data Compression Architecture for Irregular Wireless Sensor Networks

“…Low-level networking techniques have been proposed in order to help route data within the sensor network with the hope of minimizing duplicated packets and minimizing the number of hops needed to deliver the data. Finally, data compression techniques are emerging for such sensor networks [3] [4] [5] [6] [7] [8].…”