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Optimal rate allocation for entropy-coded uniform scalar quantization of dependent sources in nonbinary hypothesis testing

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3 Author(s)
Ali Tabesh ; The University of Arizona, Tucson ; Michael W. Marcellin ; Mark A. Neifeld

We propose a closed-form rate allocation scheme (RAS) for entropy-coded uniform scalar quantization of dependent sources in classification problems. The proposed RAS is applicable to nonbinary classification with piecewise monotonic unquantized Bayes decision boundaries. The RAS is also extended to joint compression and classification.

Published in:

IEEE Transactions on Communications  (Volume:58 ,  Issue: 1 )