Generalized scalar quantizer design using dynamic programming

Abstract

We cast the design of generalized scalar quantizers as a dynamic programming problem. The algorithm enables the design of discrete nonparametric estimators directly from training data and has the advantage of admitting a variety of constraints on the estimator mapping. The utility of the algorithm is illustrated with the design of rate-distortion performance predictors for a video coder.