Distributed Coding of Multiview Sparse Sources with Joint Recovery

Conference contribution


Publication Details

Author(s): van Luong H, Deligiannis N, Forchhammer S, Kaup A
Publication year: 2016
ISBN: 978-1-5090-5966-9
ISSN: 2472-7822
Language: English


Abstract

In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computational and energy constraints at each camera as well as the limitations regarding inter-camera communication. Our approach addresses these challenges by exploiting the sparsity of the visual descriptor histograms as well as their intra- and inter-camera correlations. Our method couples distributed source coding of the sparse sources with a new joint recovery algorithm that incorporates multiple side information signals, where prior knowledge (low quality) of all the sparse sources is initially sent to exploit their correlations. Experimental evaluation using the histograms of shift-invariant feature transform (SIFT) descriptors extracted from multiview images shows that our method leads to an average bit-rate saving of 30.7% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources.


FAU Authors / FAU Editors

Kaup, André Prof. Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
van Luong, Huynh
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


How to cite

APA:
van Luong, H., Deligiannis, N., Forchhammer, S., & Kaup, A. (2016). Distributed Coding of Multiview Sparse Sources with Joint Recovery. Nuremberg, DE.

MLA:
van Luong, Huynh, et al. "Distributed Coding of Multiview Sparse Sources with Joint Recovery." Nuremberg 2016.

BibTeX: 

Last updated on 2019-19-04 at 03:10