Bit precision analysis for compressed sensing
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
On the empirical rate-distortion performance of compressive sensing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Sparse representation-based face recognition for one training image per person
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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Compressive sensing (CS) is a new signal acquisition technique for sparse and com- pressible 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 quantization of strictly sparse, power-limited signals and concludes that CS with scalar quantization uses its allocated rate inefficiently.