Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compres-sive sensing and examines practical quantization strategies for compressive acquisition systems. Specif-ically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delt...
Abstract We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS)...
International audienceFollowing the Compressed Sensing (CS) paradigm, this paper studies the problem...
In this paper we study the problem of reconstructing sparse or compressible signals from compressed ...
Quantization is an essential step in digitizing signals, and, therefore, an indispensable component ...
Quantization is an essential step in digitizing signals, and, therefore, an indispensable component ...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
Summary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007...
The paper aims to highlight relative strengths and weaknesses of some of the recently proposed archi...
The paper aims to highlight relative strengths and weaknesses of some of the recently proposed archi...
Compressed Sensing (CS) is a novel mathematical framework that has revolutionized modern signal and ...
none5noThe paper aims to highlight relative strengths and weaknesses of some of the recently propose...
We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low ...
Abstract We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS)...
International audienceFollowing the Compressed Sensing (CS) paradigm, this paper studies the problem...
In this paper we study the problem of reconstructing sparse or compressible signals from compressed ...
Quantization is an essential step in digitizing signals, and, therefore, an indispensable component ...
Quantization is an essential step in digitizing signals, and, therefore, an indispensable component ...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
The field of quantized compressed sensing investigates how to jointly design a measurement matrix, q...
Summary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007...
The paper aims to highlight relative strengths and weaknesses of some of the recently proposed archi...
The paper aims to highlight relative strengths and weaknesses of some of the recently proposed archi...
Compressed Sensing (CS) is a novel mathematical framework that has revolutionized modern signal and ...
none5noThe paper aims to highlight relative strengths and weaknesses of some of the recently propose...
We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low ...
Abstract We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS)...
International audienceFollowing the Compressed Sensing (CS) paradigm, this paper studies the problem...
In this paper we study the problem of reconstructing sparse or compressible signals from compressed ...