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dc.contributor.authorBoufounos, Petros T.
Baraniuk, Richard G. 2007-01-16T20:06:46Z 2007-01-16T20:06:46Z 2007-01-16
dc.identifier.citation P. T. Boufounos and R. G. Baraniuk, "Quantization of Sparse Representations," 2007.
dc.description Summary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007, Snowbird, Utah
dc.description.abstract Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with randomized basis functions; the signal is then recovered by a convex optimization. Random CS measurements are universal in the sense that the same acquisition system is sufficient for signals sparse in any representation. This paper examines the effect of quanitization of CS measurements. A careful study of stictly sparse, power-limited signals concludes that CS with scalar quantization does not use its allocated rate efficiently. The inefficiency, which is quantified, can be interpreted as the price that must be paid for the universality of the encoding system. The results in this paper complement and extend recent results on the quantization of compressive sensing measurements of compressible signals.
dc.description.sponsorship Research supported by ONR grants N00014-06-1-0768 and N00014-06-1-0829; AFOSR grant FA9550-04-0148; DARPA grants N66001-06-1-2011 and N00014-06-1-0610; NSF grants CCF-0431150, CNS-0435425, and CNS-0520280; and the Texas Instruments Leadership University Program.
dc.format.extent 139380 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.IsPartOfSeries Rice University ECE Department Technical Report 0701
compressive sensing
sparse signals
dc.title Quantization of Sparse Representations
dc.type Report
dc.type.dcmi Text
dc.type.dcmi Text

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