Even with modern graphics hardware, it is still not possible to achieve high fidelity renderings of complex scenes in real time. However, as these images are produced for human observers we may exploit the fact that the human eye is good, but not that good. In particular, it may be possible to render parts of an image at high quality and the rest of the scene at lower quality without the user being aware of this difference. Image quality assessment algorithms, such as the Daly model, provide a measure of the perceptual quality difference between image pairs. This paper presents a psychophysical evaluation of an image quality metric and investigates how such models can be developed to rapidly determine the parts of the scene with the most noticeable perceptual difference.