| dc.description.abstract | At the receiving end of visual data are humans; thus it is only natural to takeinto account various properties and limitations of the human visual system whiledesigning new image and video processing methods. In this dissertation we buildmultiple models of human vision with di?erent focuses and complexities, anddemonstrate their use in computer graphics context.The human visual system models we present perform two fundamental tasks:predicting the visual signi?cance, and the detection of visual features. We startby 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. Anothermore comprehensive model including mechanisms to simulate maladaptation isused to predict the visual signi?cance of images shown on display devices underdynamically changing lighting conditions.The detection task is investigated in the context of image and video qualityassessment. We present an extension to commonly used image quality metricsthat enables HDR support while retaining backwards compatibility with LDRcontent. We also propose a new 'dynamic range independent' image qualityassessment method that can compare HDR-LDR (and vice versa) reference-testimage pairs, in addition to image pairs with the same dynamic range. Furthermore, the design and validation of a dynamic range independent video qualityassessment 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 tonemapping operator evaluation. | en_US |
