The rapid development of digital image processing and machine learning has increased the need for reliable and task-appropriate evaluation methods. However, the wide range of available metrics, each with different assumptions, strengths, and limitations, makes metric selection challenging across diverse image-processing tasks. This article presents a task-oriented review of commonly used evaluation metrics and proposes a practical framework for selecting appropriate metrics across five major categories: image quality assessment, image classification, image segmentation, image restoration, and object detection. Representative metrics are reviewed based on their mathematical basis, interpretat... More >