Quantization of Sparse Representations

dc.contributor.authorBoufounos, Petros T.
dc.contributor.authorBaraniuk, Richard G.
dc.date.accessioned2007-01-16T20:06:46Z
dc.date.available2007-01-16T20:06:46Z
dc.date.issued2007-01-16
dc.descriptionSummary to appear in the Proceedings of the Data Compression Conference (DCC) '07, March 27-29, 2007, Snowbird, Utah
dc.description.abstractCompressive 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.sponsorshipResearch 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.extent139380 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.citationP. T. Boufounos and R. G. Baraniuk, "Quantization of Sparse Representations," 2007.
dc.identifier.urihttps://hdl.handle.net/1911/13033
dc.language.isoeng
dc.relation.IsPartOfSeriesRice University ECE Department Technical Report 0701
dc.subjectquantization
dc.subjectcompressive sensing
dc.subjectsparse signals
dc.titleQuantization of Sparse Representations
dc.typeReport
dc.type.dcmiText
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