Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Umme Sara,Mohammad Shorif Uddin,Morium Akter

doi:10.4236/jcc.2019.73002

Abstract

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

Highlights

Image Quality Assessment (IQA) is considered as a characteristic property of an image
Image quality is being assessed by full reference metrics, like mean squared error (MSE) (Mean Square Error) and peak signal-to-noise ratio (PSNR) (Peak Signal to Noise Ratio)
Structured similarity indexing method (SSIM) and feature similarity indexing method (FSIM) can be treated more understandable than the MSE and PSNR

Summary

Introduction

Image Quality Assessment (IQA) is considered as a characteristic property of an image. Image quality assessment metrics such as MSE, PSNR are mostly applicable as they are simple to calculate, clear in physical meanings, and convenient to implement mathematically in the optimization context. They are sometimes very mismatched to perceive visual quality and are not normalized in representation. Structured similarity indexing method (SSIM) gives normalized mean value of structural similarity between the two images and feature similarity indexing method (FSIM) gives normalized mean value of feature similarity between the two images All these are full-reference image quality measurement metrics. We compare the FSIM, SSIM, MSE and PSNR values between the two images (an original and a recovered image) from denoising for different noise concentrations

Quality Measurement Technique

Experimental Results and Discussions

Conclusion