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Optimal quantization of random measurements in compressed sensing

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2 Author(s)
John Z. Sun ; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, 02139, USA ; Vivek K. Goyal

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.

Published in:

2009 IEEE International Symposium on Information Theory

Date of Conference:

June 28 2009-July 3 2009