@article{74c5540a7b7a447fa022825ccbde8032,
title = "Compressive sampling and lossy compression: Do random measurements provide an efficient method of representing sparse signals?",
abstract = "A recent work investigated whether random measurements of sparse signals provide an efficient method of representing sparse signals. How the random measurements are encoded into bits are the basis for the performance of the source coding. Even very weak forms of universality are precluded using familiar forms of quantization. The recent work also showed that recovery of the sparsity pattern is asymptotically impossible and that the mean-squared error peformance is far from optimal.",
author = "Goyal, {Vivek K.} and Fletcher, {Alyson K.} and Sundeep Rangan",
note = "Funding Information: Alyson K. Fletcher received the B.S. degree in mathematics from the University of Iowa and the M.A. degree in mathematics and the M.S. and Ph.D. degrees in electrical engineering from the University of California, Berkeley. She is currently a President{\textquoteright}s Postdoctoral Fellow at the University of California, Berkeley. She is a member of SWE, SIAM, and Sigma Xi. She has been awarded the University of California Eugene L. Lawler Award, the Henry Luce Foundation{\textquoteright}s Clare Boothe Luce Fellowship, and the Soroptimist Dissertation Fellowship. Her research interests include estimation, image processing, statistical signal processing, sparse approximation, wavelets, and control theory.",
year = "2008",
month = mar,
doi = "10.1109/MSP.2007.915001",
language = "English (US)",
volume = "25",
pages = "48--56",
journal = "IEEE Signal Processing Magazine",
issn = "1053-5888",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",
}