Quantization of Sparse Representations
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 quantization of CS measurements. A careful study of strictly 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2007
- Accession Number
- ADA521805
Entities
People
- Petros Boufounos
- Richard G. Baraniuk
Organizations
- Rice University