SNR Based Adaptive Quantized Iterative Thresholding for Sparse Approximation

Abstract

Different compression techniques necessitate the signal to be sparse or have a sparse representation in a suitable domain. Among these methods, compressed sensing is regarded as highly efficient. Sparse coding seeks to identify a sparse group of coefficients that effectively depicts a signal from a predetermined overcomplete dictionary. In the context of practical signal processing, quantization of sampled signals marks a crucial step in digitizing signals, and subsequently, realizing their high-efficiency transmission. In this study, we introduce a novel approach that integrates the challenges inherent in the approximation of sparse signals with coefficient quantization. In this study, a new method called Adaptive Quantized Iterative Thresholding is introduced, which utilizes Signal-to-Noise Ratio (SNR) to enhance classical iterative thresholding techniques for identifying sparse signal representations. Simulation outcomes demonstrate a notable enhancement in operational SNR performance when compared to Quantized Iterative Thresholding methods.