Channel-Optimized Vector Quantizer Design for Compressed Sensing Measurements
Information Theory
2014-05-01 v1 math.IT
Abstract
We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account.
Keywords
Cite
@article{arxiv.1404.7648,
title = {Channel-Optimized Vector Quantizer Design for Compressed Sensing Measurements},
author = {Amirpasha Shirazinia and Saikat Chatterjee and Mikael Skoglund},
journal= {arXiv preprint arXiv:1404.7648},
year = {2014}
}
Comments
Published in ICASSP 2013