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Study On Different Image Quality Assessment Techniques For Gray Scale Images
Subject area: Science,Engineering and Technology · Area of research: Computer Science And Engineering
Abstract
Quality is a very important parameter for all objects and their functionalities. For authentic 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 MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, SSIM (Structured Similarity Indexing Method) is proposed which compares the structural measure between obtained and original images. This paper is mainly stressed on SSIM and compares the finding with MSE and PSNR. To measure the image quality we have done a simulation work by adding noise to bench-marked original images and then calculated MSE, PSNR and SSIM of the corresponding image. We have found the superiority of the SSIM in comparison to MSE and PSNR.
Keywords
Image Quality, Computer Simulation, Salt & Pepper noise, Gaussian noise.
How to cite this paper
@article{1701020,
author = {Umme Sara},
title = {Study On Different Image Quality Assessment Techniques For Gray Scale Images},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {2},
number = {9},
pages = {18-24},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1701020.pdf},
abstract = {Quality is a very important parameter for all objects and their functionalities. For authentic 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 MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, SSIM (Structured Similarity Indexing Method) is proposed which compares the structural measure between obtained and original images. This paper is mainly stressed on SSIM and compares the finding with MSE and PSNR. To measure the image quality we have done a simulation work by adding noise to bench-marked original images and then calculated MSE, PSNR and SSIM of the corresponding image. We have found the superiority of the SSIM in comparison to MSE and PSNR.},
keywords = {Image Quality, Computer Simulation, Salt & Pepper noise, Gaussian noise.},
month = {March},
}