Models of visual attention have many applications including but not limited to rendering, advertising, graphic design and road safety. The rise of high fidelity imaging technologies, such as high dynamic range content and physically-based rendering have created a need for more targeted models, however the data necessary for their creation is sparse. This thesis aims to expand the applicability of visual attention frameworks for high fidelity imaging both by introducing a new selective rendering method for adaptively adjusting the quality of rendered scenes and developing the necessary tools to validate existing and future models in high fidelity domains. This thesis first presents a method for exploiting visual attention, in a Physically- Based-Rendering (PBR) pipeline, by adjusting complexity of Bidirectional Reflectance Distribution Functions (BRDFs) in unimportant image regions. Thus, the presented method substitutes high accuracy, high cost models with low accuracy, low cost models in less salient regions. The efficacy of this method is evaluated through a subjective rating experiment. The results of the psychophysical experiment found some significant confusion between the hybrid and references images, which suggests that this can be employed as a tool to reduce computational costs. Furthermore, this thesis presents an experiment to assess the effect of high luminance levels on the viewing strategies of observers. This is accomplished through the creation of an HDR eye-tracking dataset consisting of eighty HDR images, shown at four distinct brightness levels. A statistical analysis of the resulting fixation density maps found that the reliability of LDR eye tracking data decreases as the peak brightness of an images increases. This suggests the need for reliable HDR eye-tracking datasets. Finally, this thesis presents an eye-tracking experiment and subjective survey to analyse the interaction of ambient light levels and screen brightness on visual fatigue and visual saliency. Results of the experiment show an increase in similarity between HDR and LDR fixations as environmental illumination increases, this is of particular note as standard practice calls for eye-tracking dataset to be captured in dark environments.