Analysis of Image Quality Assessment Methods and Metrics: A Comprehensive Review

Abstract

The evaluation of metrics, the effects of machine learning, and the use of preprocessing techniques for image enhancement constitute the essence of image quality. Through a thorough investigation, this study seeks to identify the core components of image quality. First, various measures for gauging image quality are compared to see how well they work. These metrics offer precise evaluations of aspects of a picture such as sharpness, contrast, and color integrity. The effect of machine learning techniques on the quality of images is then examined. The study investigates the use of machine learning approaches to improve image quality by training models on substantial datasets and tailoring them for certain tasks. In addition, the function of preprocessing methods in picture enhancement is investigated. Before further processing or analysis, the image quality is improved using various techniques, including noise reduction, image denoising, and local entropy filtering. The findings of this work offer important new perspectives on the assessment of image quality measurements, the potential of machine learning for image enhancement, and the significance of preprocessing methods in producing higher image quality.