Introduction to human perception   “The goal of computer graphics is not to control light, but to control our perception   of light. Light is merely a carrier of the information we gather by perception.”   (Jack Tumblin, James A. Ferwerda)   Outputs of computer graphics are intended to be observed by human subjects. As human   vision has several limitations, the knowledge of the human visual system (HVS) and of the   human perception can be utilized to improve the performance of various computer graphics   algorithms. In the field of computer graphics the knowledge of the human visual system usually   takes the form of the computational models of human vision. Such a model can be incorporated   at various areas of computer graphics.   1   One of the areas where the incorporation of human vision models is extremely beneficial is the   image quality assessment and the image comparison. Image quality assessment and comparison   metrics play an important role in various computer graphics applications. They can be used to   monitor image quality for quality control systems, they can be employed to benchmark image   processing algorithms, and they can be embedded into an image processing system to optimize   the algorithms and the parameter settings. It is well known [49], that classical comparison   metrics like Root Mean Square (RMS) error are not sufficient when applied to the comparison   of images, because they poorly predict the differences between the images as perceived by   the human observer. To solve the problem properly the visual differences predictors have   evolved. The main part of visual differences predictors is typically a model of early vision, so   that they perform well when comparing visually very near images. However their performance   when comparing quite different images with respect to the contained information is poor. The   predictor capable to incorporate such a behaviour would be valuable in the image database   retrievals, to evaluation of the perceptual impact... [continues]

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