Quantization for Compressed Sensing Reconstruction

John Z. Sun (1) , Vivek K. Goyal (1)

1 EECS - Department of Electrical Engineering and Computer Science

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.

Domaines: Théorie de l'information et codage [math.IT]; Théorie de l'information [cs.IT]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

John Z. Sun, Vivek K. Goyal. Quantization for Compressed Sensing Reconstruction. SAMPTA'09, May 2009, Marseille, France. Special session on sampling and quantization. ⟨hal-00452256⟩