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Compressive Sampling and Lossy Compression

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3 Author(s)

Recent results in compressive sampling have shown that sparse signals can be recovered from a small number of random measurements. This property raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense. Through both theoretical and experimental results, we show that encoding a sparse signal through simple scalar quantization of random measurements incurs a significant penalty relative to direct or adaptive encoding of the sparse signal. Information theory provides alternative quantization strategies, but they come at the cost of much greater estimation complexity.

Published in:

Signal Processing Magazine, IEEE  (Volume:25 ,  Issue: 2 )

Date of Publication:

March 2008

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