Image Quality Assessment Through FSIM, SSIM, MSE and PSNR—A Comparative Study
Journal of Computer and Communications, 2019, 7, 8-18 http://www.scirp.org/journal/jcc ISSN Online: 2327-5227 ISSN Print: 2327-5219 Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study Umme Sara1, Morium Akter2, Mohammad Shorif Uddin2 1National Institute of Textile Engineering and Research, Dhaka, Bangladesh 2Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh How to cite this paper: Sara, U., Akter, M. Abstract and Uddin, M.S. (2019) Image Quality As- sessment through FSIM, SSIM, MSE and Quality is a very important parameter for all objects and their functionalities. PSNR—A Comparative Study. Journal of In image-based object recognition, image quality is a prime criterion. For au- Computer and Communications, 7, 8-18. thentic image quality evaluation, ground truth is required. But in practice, it https://doi.org/10.4236/jcc.2019.73002 is very difficult to find the ground truth. Usually, image quality is being as- Received: January 30, 2019 sessed by full reference metrics, like MSE (Mean Square Error) and PSNR Accepted: March 1, 2019 (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two Published: March 4, 2019 more full reference metrics SSIM (Structured Similarity Indexing Method) Copyright © 2019 by author(s) and and FSIM (Feature Similarity Indexing Method) are developed with a view to Scientific Research Publishing Inc. compare the structural and feature similarity measures between restored and This work is licensed under the Creative original objects on the basis of perception. This paper is mainly stressed on Commons Attribution International License (CC BY 4.0).
[Show full text]