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Human Visual System Models in Computer Graphics

At the receiving end of visual data are humans; thus it is only natural to take into account various properties and limitations of the human visual system while designing new image and video processing methods. In this dissertation we build multiple models of human vision with different focuses and complexities, and demonstrate their use in computer graphics context. The human visual system models we present perform two fundamental tasks: predicting the visual significance, and the detection of visual features. We start by showing that a perception based importance measure for edge strength prediction results in qualitatively better outcomes compared to commonly used gradient magnitude measure in multiple computer graphics applications. Another more comprehensive model including mechanisms to simulate maladaptation is used to predict the visual significance of images shown on display devices under dynamically changing lighting conditions. The detection task is investigated in the context of image and video quality assessment. We present an extension to commonly used image quality metrics that enables HDR support while retaining backwards compatibility with LDR content. We also propose a new “dynamic range independent” image quality assessment method that can compare HDR-LDR (and vice versa) reference-test image pairs, in addition to image pairs with the same dynamic range. Furthermore, the design and validation of a dynamic range independent video quality assessment method, that models various spatiotemporal aspects of human vision, is presented along with pointers to a wide range of application areas including comparison of rendering qualities, HDR compression and temporal tone mapping operator evaluation.