high performance computing on graphics processing units: hgpu.org

The quality of the available hardware texture filtering, even on state of the art graphics hardware, suffers from several aliasing artifacts, in both spatial and temporal domain. Those artifacts are mostly evident in extreme conditions, such as grazing viewing angles, highly warped texture coordinates, or extreme perspective and become especially annoying when animation is involved. In this paper we introduce a method to perform high quality texture filtering on GPU, based on the theory behind the Elliptical Weighted Average (EWA) filter. Our method uses the underlying anisotropic filtering hardware of the GPU to construct a filter that closely matches the shape and the properties of the EWA filter, offering vast improvements in the quality of texture mapping while maintaining high performance. Targeting real-time applications, we also introduce a novel spatial and temporal sample distribution scheme that distributes samples in space and time, permitting the human eye to perceive a higher image quality, while using less samples on each frame. Those characteristics make our method practical for use in games and other interactive applications. For cases where quality is more important than speed, like GPU renderers and image manipulation programs, we also present an exact implementation of the EWA filter that smartly uses the underlying bilinear filtering hardware to gain a significant speedup.