Paper Info
Title
A Reconstruction Algorithm with Multiple Side Information for Distributed Compression of Sparse Sources
Abstract
We consider the task of reconstructing target signals which are processed as sparse sources for a distributed compression scenario, where communication between the sources is prohibited, however, correlation of information among sources can be utilized at the decoder. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI) for such distributed sparse sources. The proposed algorithm takes advantage of both a compressive sensing (CS) reconstruction with SI and an iteratively weighted ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization by solving a general weighted multi-ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (or n-ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) minimization. To utilize the known multiple SIs, the algorithm computes optimal weights on not only each individual SI but among SIs where the weights are adaptively updated according to changes at every iteration of the reconstruction. By this optimization, the proposed reconstruction algorithm with multiple SI (RAMSI) can robustly exploit the multiple SIs with different qualities. We experimentally demonstrate our algorithm on compressing feature histograms as sparse sources which are extracted from a multi-view image database for multi-view recognition. The results show that the RAMSI with multiple SIs efficiently outperforms the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization and also the CS reconstruction with only one SI.
Year
DOI
Venue
2016
10.1109/DCC.2016.95
2016 Data Compression Conference (DCC)
Keywords
Field
DocType
multiple side information,distributed sparse source compression,target signal reconstruction algorithm,signal processing,information correlation,decoder,compressive sensing reconstruction,iteratively weighted l1-norm minimization,general weighted multil1 minimization,optimal weights,adaptively updated weights,reconstruction algorithm-with-multiple SI,RAMSI,feature histogram compression,sparse sources,multiview image database,multiview recognition,CS reconstruction
Iterative reconstruction,Compression (physics),Histogram,Computer science,Side information,Theoretical computer science,Reconstruction algorithm,Minification,Decoding methods,Compressed sensing
Conference
ISSN
ISBN
Citations 
1068-0314
978-1-5090-1854-3
0
PageRank 
References 
Authors
0.34
9
4
Name
Order
Citations
PageRank
Huynh Van Luong15010.25
Jürgen Seiler214528.28
André Kaup3861127.24
Søren Forchhammer440653.36