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 com pare this q uantizer to other practical designs and show a noticeable improvement in the operational distortion-rate performance.
Recently, an optimal quantization strategy for the random measurements has been designed based on distributed functional scalar quantizers REF .
Optimal quantization of random measurements in compressed sensing
2009 IEEE International Symposium on Information Theory
2009 IEEE International Symposium on Information Theory
Computer Science