Analysis of wavelet-based full reference image quality assessment algorithm

Faizah Mokhtar,Taha Basheer,Amir Nazren Abdul Rahim,Ruzelita Ngadiran

doi:10.11591/eei.v8i2.1404

Abstract

Measurement of Image Quality plays an important role in numerous image processing applications such as forensic science, image enhancement, medical imaging, etc. In recent years, there is a growing interest among researchers in creating objective Image Quality Assessment (IQA) algorithms that can correlate well with perceived quality. A significant progress has been made for full reference (FR) IQA problem in the past decade. In this paper, we are comparing 5 selected FR IQA algorithms on TID2008 image datasets. The performance and evaluation results are shown in graphs and tables. The results of quantitative assessment showed wavelet-based IQA algorithm outperformed over the non-wavelet based IQA method except for WASH algorithm which the prediction value only outperformed for certain distortion types since it takes into account the essential structural data content of the image.

Highlights

Digital images often pass through several processing stages such as acquisition, processing, storage and transmission before they reach to the observers [1]
It uses six simple 2D Haar wavelet filters to detect vertical and horizontal edges. This metric can be appraised as a simplified interpretation of Feature SIMilarity index (FSIM)
A comparison has been done between the five image quality assessment (IQA) metrics by simulating them using MATLAB

Summary

INTRODUCTION

Digital images often pass through several processing stages such as acquisition, processing, storage and transmission before they reach to the observers [1]. The perfect method of quantifying image quality is through subjective evaluation To evaluate this type of measurement, a number of observers are selected, tested for their visual capabilities, shown a series of test scenes and asked to score the quality of the scenes [4]. Objective measurement: These are automatic algorithms for quality assessment that could analyze images and report their quality without human involvement Such methods could eliminate the need for expensive subjective studies. Where MSE is the mean square error and L is the dynamic range of the pixel values This method does not correlate well with human perception of quality as it is only calculate the pixel difference between original and distorted image. The local similarities between reference and distorted images is measured by these coefficients It uses six simple 2D Haar wavelet filters to detect vertical and horizontal edges. This metric can be appraised as a simplified interpretation of Feature SIMilarity index (FSIM)

DATASET AND PERFORMANCE METRICS

RESULTS AND ANALYSIS

CONCLUSION