Lossy compression of distributed sparse sources: A practical scheme

A new lossy compression scheme for distributed and sparse sources under a low complexity encoding constraint is proposed. This architecture is able to exploit both intra- and inter-signal correlations typical of signals monitored, for example, by a wireless sensor network. In order to meet the low c...

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Veröffentlicht in:2011 19th European Signal Processing Conference S. 422 - 426
Hauptverfasser: Coluccia, G., Magli, E., Roumy, A., Toto-Zarasoa, V.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2011
Schlagworte:
Correlation
Decoding
Distortion measurement
Encoding
Joints
Quantization (signal)
Silicon
ISSN:2076-1465
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Abstract A new lossy compression scheme for distributed and sparse sources under a low complexity encoding constraint is proposed. This architecture is able to exploit both intra- and inter-signal correlations typical of signals monitored, for example, by a wireless sensor network. 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 propose a joint CS reconstruction filter, which exploits the knowledge of the side information to improve the quality 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.
AbstractList A new lossy compression scheme for distributed and sparse sources under a low complexity encoding constraint is proposed. This architecture is able to exploit both intra- and inter-signal correlations typical of signals monitored, for example, by a wireless sensor network. 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 propose a joint CS reconstruction filter, which exploits the knowledge of the side information to improve the quality 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.
Author Roumy, A.
Toto-Zarasoa, V.
Magli, E.
Coluccia, G.
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  surname: Toto-Zarasoa
  fullname: Toto-Zarasoa, V.
  organization: INRIA, Rennes, France
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Snippet A new lossy compression scheme for distributed and sparse sources under a low complexity encoding constraint is proposed. This architecture is able to exploit...
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StartPage 422
SubjectTerms Correlation
Decoding
Distortion measurement
Encoding
Joints
Quantization (signal)
Silicon
Title Lossy compression of distributed sparse sources: A practical scheme
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