Lossy Compression of Distributed Sparse Sources: a Practical Scheme

Giulio Coluccia 1 Enrico Magli 1 Aline Roumy 2 Velotiaray Toto-Zarasoa 2
2 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : A new lossy compression scheme for distributed and sparse sources under a low complexity encoding con- straint is proposed. This architecture is able to exploit both intra- and inter-signal correlations typical of sig- nals monitored, for example, by a wireless sensor net- work. In order to meet the low complexity constraint, the encoding stage is performed by a lossy distributed compressed sensing (CS) algorithm. The novelty of the scheme consists in the combination of lossy distributed source coding (DSC) and CS. More precisely, we pro- pose a joint CS reconstruction lter, which exploits the knowledge of the side information to improve the qual- ity of both the dequantization and the CS reconstruction steps. The joint use of CS and DSC allows to achieve large bit-rate savings for the same quality with respect to the non-distributed CS scheme, e.g. up to 1.2 bps in the cases considered in this paper. Compared to the DSC scheme (without CS), we observe a gain increasing with the rate for the same mean square error.
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Communication dans un congrès
2011 European Signal Processing Conference (EUSIPCO‐2011), Aug 2011, Barcelona, Spain. 2011
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Giulio Coluccia, Enrico Magli, Aline Roumy, Velotiaray Toto-Zarasoa. Lossy Compression of Distributed Sparse Sources: a Practical Scheme. 2011 European Signal Processing Conference (EUSIPCO‐2011), Aug 2011, Barcelona, Spain. 2011. 〈inria-00629001〉

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