DocumentCode :
83201
Title :
An RIP-Based Approach to 
Quantization for Compressed Sensing
Author :
Joe-Mei Feng ; Krahmer, Felix
Author_Institution :
Inst. for Numerical & Appl. Math., Univ. of Gottingen, Gottingen, Germany
Volume :
21
Issue :
11
fYear :
2014
fDate :
Nov. 2014
Firstpage :
1351
Lastpage :
1355
Abstract :
In this letter, we provide a new approach to estimating the error of reconstruction from ΣΔ quantized compressed sensing measurements. Our method is based on the restricted isometry property (RIP) of a certain projection of the measurement matrix. Our result yields simple proofs and a slight generalization of the best-known reconstruction error bounds for Gaussian and subGaussian measurement matrices.
Keywords :
compressed sensing; matrix algebra; quantisation (signal); signal reconstruction; ΣΔ quantized compressed sensing measurements; Gaussian measurement matrices; RIP-based approach; error estimation; measurement matrix; reconstruction error bounds; restricted isometry property; subGaussian measurement matrices; Accuracy; Compressed sensing; Measurement uncertainty; Minimization; Quantization (signal); Random variables; Vectors; $Sigma Delta $ quantization; compressed sensing; restricted isometry property;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2014.2336700
Filename :
6849950
Link To Document :
