Analysis-by-Synthesis-based Quantization of Compressed Sensing Measurements

TL;DR

This paper introduces a novel quantizer design for compressed sensing measurements that enhances sparse signal reconstruction quality in resource-limited scenarios by employing an analysis-by-synthesis framework.

Contribution

It proposes a new quantization method tailored for low-measurement, low-rate compressed sensing systems, improving reconstruction performance over existing approaches.

Findings

The proposed quantizer outperforms existing methods in simulations.

It effectively reconstructs sparse signals with fewer measurements and lower bit rates.

The analysis-by-synthesis approach enhances reconstruction accuracy in resource-constrained settings.

Abstract

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods.