Abstract—We study the average distortion introduced by quantizing compressive sensing measurements. Both uniform quantization and non-uniform quantization are considered. The asymptotic distortion-rate functions are obtained when the mea-surement matrix belongs to certain random ensembles. A new modification of greedy reconstruction algorithm that accommo-dates quantization errors is proposed and its performance is evaluated through extensive computer simulations. I
Summary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007...
Compressed sensing (CS) studies the recovery of a high dimensional signal from its low dimensional l...
We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low ...
Quantization is an essential step in digitizing signals, and, therefore, an indispensable component ...
Abstract We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS)...
Abstract-The problem of compressing a real-valued sparse source using compressive sensing techniques...
In this paper, we endeavor for predicting the performance of quantized compressive sensing under the...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
International audienceFollowing the Compressed Sensing (CS) paradigm, this paper studies the problem...
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low...
In this paper we study the problem of reconstructing sparse or compressible signals from compressed ...
This paper studies the problem of reconstructing sparse or compressible signals from compressed sens...
Abstract In order to save energy of low-power sensors in Internet of Things applications, minimizin...
In this paper we study the problem of recovering sparse or compressible signals from uniformly quant...
Quantization is an important but often ignored consideration in discussions about compressed sensing...
Summary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007...
Compressed sensing (CS) studies the recovery of a high dimensional signal from its low dimensional l...
We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low ...
Quantization is an essential step in digitizing signals, and, therefore, an indispensable component ...
Abstract We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS)...
Abstract-The problem of compressing a real-valued sparse source using compressive sensing techniques...
In this paper, we endeavor for predicting the performance of quantized compressive sensing under the...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
International audienceFollowing the Compressed Sensing (CS) paradigm, this paper studies the problem...
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low...
In this paper we study the problem of reconstructing sparse or compressible signals from compressed ...
This paper studies the problem of reconstructing sparse or compressible signals from compressed sens...
Abstract In order to save energy of low-power sensors in Internet of Things applications, minimizin...
In this paper we study the problem of recovering sparse or compressible signals from uniformly quant...
Quantization is an important but often ignored consideration in discussions about compressed sensing...
Summary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007...
Compressed sensing (CS) studies the recovery of a high dimensional signal from its low dimensional l...
We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low ...