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

• 作者: Mohammad Shorif Uddin Morium Akter Umme Sara
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: Image Quality COMPUTER Simulation GAUSSIAN Noise DENOISING
• 年，卷（期）: dnhtxyw_2019,（3）
• 页码范围: 8-18
• 页数: 11页
• 分类号: TP39

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.