Computer Science – Information Theory
Scientific paper
2008-11-21
Computer Science
Information Theory
36 pages, 10 figures
Scientific paper
Communication of quantized information is frequently followed by a computation. We consider situations of \emph{distributed functional scalar quantization}: distributed scalar quantization of (possibly correlated) sources followed by centralized computation of a function. Under smoothness conditions on the sources and function, companding scalar quantizer designs are developed to minimize mean-squared error (MSE) of the computed function as the quantizer resolution is allowed to grow. Striking improvements over quantizers designed without consideration of the function are possible and are larger in the entropy-constrained setting than in the fixed-rate setting. As extensions to the basic analysis, we characterize a large class of functions for which regular quantization suffices, consider certain functions for which asymptotic optimality is achieved without arbitrarily fine quantization, and allow limited collaboration between source encoders. In the entropy-constrained setting, a single bit per sample communicated between encoders can have an arbitrarily-large effect on functional distortion. In contrast, such communication has very little effect in the fixed-rate setting.
Goyal Vivek K.
Misra Vinith
Varshney Lav R.
No associations
LandOfFree
Distributed Scalar Quantization for Computing: High-Resolution Analysis and Extensions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Distributed Scalar Quantization for Computing: High-Resolution Analysis and Extensions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distributed Scalar Quantization for Computing: High-Resolution Analysis and Extensions will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-163881