High-Resolution Functional Quantization

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High-Resolution Functional Quantization
Suppose a function of N real source variables XN 1 = (X1, X2, . . . , XN ) is desired at a destination constrained to receive a limited number of bits. If the result of evaluating the function, Y = G(XN 1 ), can be itself encoded, this is the optimal strategy--the origin of Y becomes irrelevant to the communication problem. We consider two alternative scenarios: distributed quantization, in which each Xi must be separately encoded; and linear transform coding of XN 1 . Optimal fixed- and variable-rate scalar quantizers are derived under the conventional assumptions of high-resolution quantization theory, and we find optimal transforms for transform coding. For certain classes of functions, examples demonstrate large improvements over using quantizers designed to minimize distortion of the Xis.
Vinith Misra, Vivek K. Goyal, Lav R. Varshney
Added 25 Dec 2009
Updated 25 Dec 2009
Type Conference
Year 2008
Where DCC
Authors Vinith Misra, Vivek K. Goyal, Lav R. Varshney
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