Low-complexity compressive sensing with downsampling
Cited 0 times inCited 0 times in
- Low-complexity compressive sensing with downsampling
- Lee, Dongeun; Choi, Jaesik; Shin, Heonshik
- Issue Date
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- IEICE ELECTRONICS EXPRESS, v.11, no.3, pp.20130947
- Compressive sensing (CS) with sparse random matrix for the random sensing basis reduces source coding complexity of sensing devices. We propose a downsampling scheme to this framework in order to further reduce the complexity and improve coding efficiency simultaneously. As a result, our scheme can deliver significant gains to a wide variety of resource-constrained sensors. Experimental results show that the computational complexity decreases by 99.95% compared to other CS framework with dense random measurements. Furthermore, bit-rate can be saved up to 46.29%, by which less bandwidth is consumed.
- Appears in Collections:
- EE_Journal Papers
- Files in This Item:
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.