Optimal quantization of random measurements in compressed sensing
Name
Sun-2009-Optimal quantization of random measurements in compressed sensing.pdf
Size
1.02 MB
Format
Adobe PDF
Checksum (MD5)
160c4d3b6e60a399be37f77b9add2cc9
Author(s)
Sun, John Z.
Date Issued
July 2009
Journal
IEEE International Symposium on Information Theory.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Sun, J.Z., and V.K. Goyal. “Optimal quantization of random measurements in compressed sensing.” Information Theory, 2009. ISIT 2009. IEEE International Symposium on. 2009. 6-10. © 2009, IEEE
Version
Final published version
Abstract
Quantization is an important but often ignored consideration in discussions about compressed sensing. This paper studies the design of quantizers for random measurements of sparse signals that are optimal with respect to mean-squared error of the lasso reconstruction. We utilize recent results in high-resolution functional scalar quantization and homotopy continuation to approximate the optimal quantizer. Experimental results compare this quantizer to other practical designs and show a noticeable improvement in the operational distortion-rate performance.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ISIT.2009.5205695
