A Performance Comparative Analysis of Block Based Compressive Sensing and Line Based Compressive Sensing

M Ebrahim,S H Adil,D Nawaz

doi:10.48084/etasr.1946

M Ebrahim, S H Adil + Show 1 more

Open Access

https://doi.org/10.48084/etasr.1946

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Abstract

Compressive sensing (CS) is an innovative idea that has opened new areas for viable communication of correlated data. In this paper, a comparative performance analysis of two different variants of compressive sensing i.e. block based compressive sensing (BCS) and line based compressive censing (LCS) schemes is performed for natural images. The idea is to evaluate which variant performs better in terms of reconstruction quality and provides easy initial solution. The experimental analysis demonstrates that LCS scheme can enhance the image reconstruction at lower subrates by 0.5 dB to 2.5 dB, when compared to the BCS scheme.

Highlights

Compressive sensing (CS) is one of the latest techniques that have achieved popularity recently and is applied to various imaging applications [1], such as magnetic resonance imaging (MRI) [2, 3] and seismic identification [4]
The down sampled images are challenging in contrast to standard due to their larger disparity range and higher percentage of un-textured surface
We investigated the effect of block size on the image reconstruction quality

Summary

INTRODUCTION

Compressive sensing (CS) is one of the latest techniques that have achieved popularity recently and is applied to various imaging applications [1], such as magnetic resonance imaging (MRI) [2, 3] and seismic identification [4]. The block-based approach is far more developed and widely employed as compared to the newly developed line based approach to reduce of the computationally complexity of CS scheme [9,10,11,12,13,14]. Such methodologies value CS because (i) block/line based estimation is more convenient for applications where the sample image data don't need to be encoded completely in a block/line form until the point when the estimation of the whole image is completed, (ii) the application and capacity of the estimation operator are straightforward, (iii) the individual handling of each block/line of image data brings about simple initial solution with considerably quick and better recovery process.

THEORY OF COMPRESSIVE SENSING

Decoding

CONCLUSION