Analysis-by-Synthesis Quantization for Compressed Sensing Measurements

TL;DR

This paper introduces a novel analysis-by-synthesis quantization method for compressed sensing measurements, optimizing low-bit-rate measurements for improved sparse signal reconstruction in resource-limited scenarios.

Contribution

It proposes a new source coding algorithm that enhances reconstruction quality by jointly considering measurement quantization effects and sparse signal recovery.

Findings

Outperforms existing quantization schemes in simulations

Improves reconstruction quality at low bit-rates

Demonstrates effectiveness in resource-constrained sensing scenarios

Abstract

We consider a resource-limited scenario where a sensor that uses compressed sensing (CS) collects a low number of measurements in order to observe a sparse signal, and the measurements are subsequently quantized at a low bit-rate followed by transmission or storage. For such a scenario, we design new algorithms for source coding with the objective of achieving good reconstruction performance of the sparse signal. Our approach is based on an analysis-by-synthesis principle at the encoder, consisting of two main steps: (1) the synthesis step uses a sparse signal reconstruction technique for measuring the direct effect of quantization of CS measurements on the final sparse signal reconstruction quality, and (2) the analysis step decides appropriate quantized values to maximize the final sparse signal reconstruction quality. Through simulations, we compare the performance of the proposed quantization algorithms vis-a-vis existing quantization schemes.