Optimal quantization of random measurements in compressed sensing

• Main Author: Sun, John Z. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor), Goyal, Vivek K. (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers, 2011-03-07T23:03:23Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Science Foundation (U.S.) (CAREER Award CCF-643836)
Lincoln Laboratory. Advanced Concepts Committee (Air Force contract FA8721-05-C-0002)