@inbook{7943afffbab046608777364767e97165,
title = "Quantization and compressive sensing",
abstract = "Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, 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-Delta (Σ Δ) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms, and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.",
keywords = "Compressive sensing, Dual frame, Reconstruction error, Scalar quantization, Sparse signal",
author = "Boufounos, {Petros T.} and Laurent Jacques and Felix Krahmer and Rayan Saab",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.",
year = "2015",
doi = "10.1007/978-3-319-16042-9_7",
language = "English",
series = "Applied and Numerical Harmonic Analysis",
publisher = "Springer International Publishing",
number = "9783319160412",
pages = "193--237",
booktitle = "Applied and Numerical Harmonic Analysis",
edition = "9783319160412",
}