Methods for Quantized Compressed Sensing
Information Theory
2016-01-01 v1 math.IT
Numerical Analysis
Abstract
In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements. We also introduce two new greedy approaches for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP) and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding (AOP-QIHT). We compare the performance of greedy quantized compressed sensing algorithms for a given bit-depth, sparsity, and noise level.
Keywords
Cite
@article{arxiv.1512.09184,
title = {Methods for Quantized Compressed Sensing},
author = {Hao-Jun Michael Shi and Mindy Case and Xiaoyi Gu and Shenyinying Tu and Deanna Needell},
journal= {arXiv preprint arXiv:1512.09184},
year = {2016}
}