A spatio-chromatic colour appearance model for retargeting high dynamic range image appearance across viewing conditions

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This project will investigate human perception in the context of novel high dynamic range display technologies. Specifically, it will devise and validate a new model of spatial colour vision that will support detailed analysis and prediction of how content on new displays will be perceived. Such a model can then be used to automatically process images so that their appearance is preserved when presented in a significantly different manner: at different brightness levels (display dimming), at different contrast (tone-mapping, ambient light compensation), under different viewing conditions (dark cinema vs. bright living room). The model will also be able to take into account potential individual differences in observer sensitivity and implement the effect of age-related changes in the visual system.To build such a model, a large dataset of colour appearance data will be collected from both existing sources and from new measurements. A new method will be devised and used to take new measurements only to fill in the "gaps" in the dataset, where information is the sparsest. The new appearance model will be created by simultaneously training and testing a large collection of candidate models, which will be tested for overfitting using information criteria. The candidate models will further help to identify the gaps in the existing dataset and will thus direct collection of new data. The model will be tested in novel applications, such as adjustment of image appearance depending on the user's visual performance (age adaptive rendering), and adjustment for display brightness, contrast and ambient illumination (display adaptive rendering).