Pith. sign in

REVIEW

Methods for Quantized Compressed Sensing

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1512.09184 v1 pith:CNRSFFGC submitted 2015-12-30 cs.IT cs.NAmath.ITmath.NA

Methods for Quantized Compressed Sensing

Hao-Jun Michael Shi , Mindy Case , Xiaoyi Gu , Shenyinying Tu , Deanna Needell This is my paper
classification cs.IT cs.NAmath.ITmath.NA
keywords quantizedcompressedgreedysensingalgorithmscompareperformancepursuit
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.