This review is focused on image quality model building, particularly in the context of the Image Quality Circle. There are two fundamentally different ways to modeling image quality; the impairment approach and the quality approach. Impairment looks at decreases in image quality from some reference or ideal. The quality approach attempts to model the judgment of image quality directly, independent of the reference. The more successful models are called perceptual models, and have perceptual attributes, the ness, as the dependent variables. Generalized weighted mean, or Minkowski metrics, are the most successful mathematical forms of image quality models. Several issues impeding implementation of image quality models remain; appropriate psychometric scaling of quality and nesses, and identification of the nesses, particularly for image coding, compression and processing applications. The Universal Image Quality Model is not on the horizon.
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