The computation of high-fidelity images in real-time remains one of the key challenges for computer graphics. Recent work has shown that by understanding the human visual system, selective rendering may be used to render only those parts to which the human viewer is attending at high quality and the rest of the scene at a much lower quality. This can result in a significant reduction in computational time, without the viewer being aware of the quality difference. Selective rendering is guided by models of the human visual system, typically in the form of a 2D saliency map, which predict where the user will be looking in any scene. Computation of these maps themselves often take many seconds, thus precluding such an approach in any interactive system, where many frames need to be rendered per second. In this paper we present a novel saliency map which exploits the computational performance of modern GPUs. With our approach it is thus possible to calculate this map in milliseconds, allowing it to be part of a real time rendering system. In addition, we also show how depth, habituation and motion can be added to the saliency map to further guide the selective rendering. This ensures that only the most perceptually important parts of any animated sequence need be rendered in high quality. The rest of the animation can be rendered at a significantly lower quality, and thus much lower computational cost, without the user being aware of this difference.
|Translated title of the contribution||A GPU based Saliency Map for High-Fidelity Selective Rendering|
|Title of host publication||Unknown|
|Pages||21 - 29|
|Number of pages||8|
|Publication status||Published - Jan 2006|