Task-, viewer- and degradation-specific image quality assessment

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For any display system with the ultimate goal of accurately communicating visual information to its viewer, image quality and visibility are key performance metrics that must be assured. This is imperative in most visual applications, but especially prominent in safety critical display systems, for instance such as those found in the automotive industry, where crucial information must be conveyed through the instrumentation clusters and dashboards of vehicles. The perceived image quality of a display panel can be degraded in many ways. For example, in an automobile, the navigation display can be degraded by light from outside the car shining onto it, causing bothersome reflections. Or the display could appear blurred due to vibration or a layer of strawberry jam added by your children. Some viewers may be affected more or less than other viewers by a particular type of degradation (e.g. due to age-related visual deficits or varying levels of driving experience), and the sensitivity to a particular degradation may depend on the task that a user is carrying out relative to the display (e.g. whether they are reading the speedometer or getting directions from the navigation display). This project aims to develop methods to measure perceptual image quality, that are task-, viewer- and degradation-specific. These techniques will be extensions of existing, more generic, methods and will employ modern machine learning approaches. The industrial partner, Faurecia IRYStec Inc., will use the results of the project in improving their Perceptual Display Platform, which provides viewer- and task-specific display enhancements for commercial display systems, particularly those found in the automotive industry.