Information Rates of Densely Sampled Gaussian Data: Distributed Vector Quantization and Scalar Quantization With Transforms

Motivated by the question of the efficiency of dense sensor networks for sampling and encoding spatial random fields, this paper investigates the rates attainable by several lossy schemes for coding a Gaussian random field to a specified mean-squared error distortion based on sampling at asymptotically large rates. In the first, a densely sampled, spatio-temporal, stationary Gaussian source is distributively encoded. The Berger-Tung upper bound to the distributed rate-distortion function, the Szego asymptotic eigen-value theorem, and an integral convergence theorem are used to obtain an upper bound, expressed in terms of the source spectral density, to the smallest attainable rate at asymptotically high sampling densities.

Provided by: University of Michigan Topic: Networking Date Added: Dec 2011 Format: PDF

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