Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study
Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study作者机构：National Institute of Textile Engineering and Research Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh
出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)
年 卷 期：2019年第7卷第3期
页 面：8-18页
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Image Quality Computer Simulation Gaussian Noise Denoising
摘 要：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.
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