The performance of a statistical signal processing system is determined in large part by the accuracy of the probabilistic model it employs. Accurate modeling often requires working in several dimensions, but doing so can introduce dimensionality-related difficulties. A recently introduced model circumvents some of these difficulties while maintaining accuracy sufficient to account for much of the high-order, nonlinear statistical interdependence of samples. Properties of this model are reviewed, and its power demonstrated by application to image restoration and compression. Also described is a vector quantization (VQ) scheme which employs the model in entropy coding a ZN-lattice. The scheme has the advantage over standard VQ of bounding maximum instantaneous errors.
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