The image quality depends on compromises made in the design of the algorithms and devices for image capture, transfer, storage, and display. Quality assessment plays, consequently, a decisive role to successfully promote image processing algorithms and system performances. The evaluation of image quality is basically a human issue, but subjective metrics are computationally expensive and not practical for real-time applications. So, in the last decades, a great deal of efforts has been put into the development of objective quality metrics able to automatically predict perceived quality. To design an objective measure capable to be in close agreement with subjective test is a difficult task because the human visual system has a multifaceted structure and is not totally known. The tracked problem is to emulate human vision which is a cognitive activity and not a pure image sensor process. In this Chapter a classification of quality indexes, from classical to recent approaches is reported. For a better understanding of quality assessment topic, some of the principal human phenomena involved in the development of objective perceptual metrics are also explained.