We will examine objective criteria for the evaluation of image quality that are based on models of visual perception. Our primary emphasis will be on image fidelity, i.e., how close an image is to a given original or reference image, but we will broaden the scope of image fidelity to include structural equivalence. We will also discuss no-reference and limited-reference metrics. We will examine a variety of applications with special emphasis on image and video compression. We will examine near-threshold perceptual metrics, which explicitly account for human visual system (HVS) sensitivity to noise by estimating thresholds above which the distortion is just-noticeable, and supra-threshold metrics, which attempt to quantify visible distortions encountered in high compression applications or when there are losses due to channel conditions. We will also consider metrics for structural equivalence, whereby the original and the distorted image have visible differences but both look natural and are of equally high visual quality. We will also take a close look at procedures for evaluating the performance of quality metrics, including database design, models for generating realistic distortions for various applications, and subjective procedures for metric development and testing. Throughout the course we will discuss both the state of the art and directions for future research. Course topics include: • Applications: Image and video compression, restoration, retrieval, graphics, etc. • Human visual system review • Near-threshold and supra-threshold perceptual quality metrics • Structural similarity metrics • Perceptual metrics for texture analysis and compression – structural texture similarity metrics • No-reference and limited-reference metrics • Models for generating realistic distortions for different applications • Design of databases and subjective procedures for metric development and testing • Metric performance comparisons, selection, and general use and abuse • Embedded metric performance, e.g., for rate-distortion optimized compression or restoration • Metrics for specific distortions, e.g., blocking and blurring, and for specific attributes, e.g., contrast, roughness, and glossiness • Multimodal applications