Name
Abstract | ||
|---|---|---|
Signal compression is essential for energy and bandwidth efficient communication and storage systems. In this paper, we provide two practical approaches for source compression of noisy sparse and non-strictly sparse (compressible) sources. The proposed schemes are based on channel coding theory to construct a source encoder that decreases the number of transmitted bits while preserving the fidelity of the reconstructed signal at the receiver by exploiting its sparsity. In addition, a model order selection scheme is proposed to detect the non-zero elements of sparse vectors embedded in noise, or to find a nonlinear sparse approximation of compressible signals. As illustrated by numerical results, our approach provides a lower distortion-rate function compared to previously known methods. For example, the proposed schemes achieve a lower distortion, about 2 orders of magnitude, compared to compressed sensing, for the same rate. |
Year | Venue | Field |
|---|---|---|
2016 | IEEE Global Communications Conference | Noise measurement,Computer science,Sparse approximation,Algorithm,Theoretical computer science,Real-time computing,Encoder,Nonlinear distortion,Distortion,Compressed sensing,Sparse matrix,Signal compression |
DocType | ISSN | Citations |
Conference | 2334-0983 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Elzanaty | 1 | 38 | 5.72 |
| Andrea Giorgetti | 2 | 110 | 10.93 |
| Marco Chiani | 3 | 1869 | 134.93 |