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On Scalable Distributed Coding of Correlated Sources

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2 Author(s)
Ankur Saxena ; Department of Electrical & Computer Engineering, University of California, Santa Barbara, CA, USA ; Kenneth Rose

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit, a setting typically encountered in sensor networks. As communication channel conditions may vary with time, it is often desirable to guarantee a base layer of coarse information during channel fades, as well as robustness to channel link (or sensor) failures. The main contribution is twofold. First, we consider the special case of multistage distributed coding, and show that naive combination of distributed coding with multistage coding yields poor rate-distortion performance, due to underlying conflicts between the objectives of these two coding methods. An appropriate system paradigm is developed, which allows such tradeoffs to be explicitly controlled. Next, we consider the unconstrained scalable distributed coding problem. Although a standard ??Lloyd-style?? distributed coder design algorithm is easily generalized to encompass scalable coding, the algorithm performance is heavily dependent on initialization and will virtually always converge to a poor local minimum. We propose an effective initialization scheme for such a system, which employs a properly designed multistage distributed coder. We present iterative design techniques and derive the necessary conditions for optimality for both multistage and unconstrained scalable distributed coding systems. Simulation results show substantial gains for the proposed multistage distributed coding system over naive extensions which incorporate scalability in a multistage distributed coding system. Moreover, the proposed overall scalable distributed coder design consistently and substantially outperforms the randomly initialized ??Lloyd-style?? scalable distributed coder design.

Published in:

IEEE Transactions on Signal Processing  (Volume:58 ,  Issue: 5 )