Quantization constrained convex optimization for the compressive sensing reconstructions

Author

Kim, Dong Sik

Author_Institution

Sch. of Electron. & Inf. Eng., Hankuk Univ. of Foreign Studies, Yongin, South Korea

fYear

2010

fDate

14-19 March 2010

Firstpage

3898

Lastpage

3901

Abstract

In this paper, a convex optimization technique, which is based on the generalized quantization constraint (GQC), is proposed for the compressive sensing (CS) reconstruction that uses quantized measurements. The set size of the proposed GQC can be controlled, and through extensive numerical simulations based on the uniform scalar quantizers, the CS reconstruction errors are improved by 3.1-4.6dB compared to the previous quantization constraint method.

Keywords

numerical analysis; optimisation; quantisation (signal); signal sampling; compressive sensing reconstructions; generalized quantization constraint; numerical simulations; quantization constrained convex optimization; quantized measurements; uniform scalar quantizers; Constraint optimization; Error correction; Image converters; Image reconstruction; Numerical simulation; Quantization; Robustness; Sampling methods; Size control; Sparse matrices; Compressive sensing; convex optimization; generalized quantization constraint; quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5495809

Filename

5495809