Comparative Analysis of Image Quality Assessment Metrics: MSE, PSNR, SSIM and FSIM

Yusra Al Najjar

Abstract: Evaluating the quality of an image proves to be a multifaceted and intricate endeavor, given the nuanced nature of human perception influenced by an array of physical and psychological factors. Despite numerous proposed techniques aimed at measuring image quality, none emerge as flawless or universally applicable. In the realm of image processing, where precision is paramount, extensive research has explored diverse methodologies including point difference analysis, image correlation, edge detection, neural networks (NN), region of interest (ROI) analysis, and consideration of the human visual system (HVS). The essence of an effective image quality measure lies in its ability to furnish accurate and consistent predictions. This study concentrates on the full - reference image quality, which involves utilizing a reference image for comparison purposes. Additionally, the study directs its focus toward contrasting a range of comparison tools, each leveraging different metrics. Among the comparison tools scrutinized are those reliant on point - based measurements such as the mean square error (MSE) and the peak signal - to - noise ratio (PSNR). Conversely, others focus on the compositional aspects of images, exemplified by metrics like the Feature Index Matrix (FSIM) and the Structured Similarity Index Matrix (SSIM).

Keywords: FSIM, full - reference, IQM, PSNR, SSIM