Methods for Quantized Compressed Sensing

Authors

Hao-Jun Michael Shi, University of California, Los Angeles
Mindy Case, University of California, Los Angeles
Xiaoyi Gu, University of California, Los Angeles
Shenyinying Tu, University of California, Los Angeles
Deanna Needell, Claremont McKenna CollegeFollow

Document Type

Conference Proceeding

Department

Claremont McKenna College, Mathematics (CMC)

Publication Date

1-2016

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.

Rights Information

© 2016 Shi, Case, Gu, Tu, Needell

Recommended Citation

H. M. Shi, M. Case, X. Gu, S. Tu, and D. Needell. “Methods for Quantized Compressed Sensing.” Proc. Information Theory and Applications (ITA), La Jolla CA, Jan. 2016.