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High Quality Elliptical Texture Filtering on GPU

机译：GPU上的高品质椭圆纹理滤波

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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 Tenderers 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.
机译：可用硬件纹理过滤的质量，即使是最先进的图形硬件，也遭受了空间和时间域中的几个锯齿伪像。这些文物在极端条件下大多是显而易见的，例如放牧观察角度，高度扭曲的纹理坐标，或者极端的视角，并且当涉及动画时尤其烦人。在本文中，我们介绍一种在GPU上对GPU进行高质量纹理滤波的方法，基于椭圆加权平均（EWA）滤波器后面的理论。我们的方法使用GPU的底层各向异性滤波硬件来构造一个与EWA过滤器的形状和属性密切匹配的过滤器，在保持高性能的同时提供纹理映射质量的大量改进。针对实时应用，我们还介绍了一种新的空间和时间样本分布方案，其在空间和时间上分配样品，允许人眼在每个帧上使用较少的样品来感知到更高的图像质量。这些特征使我们的方法在游戏和其他交互式应用中使用。对于质量比速度更重要，如GPU投标者和图像操作计划，我们还提供了EWA过滤器的精确实现，巧妙地使用底层的双线性过滤硬件来获得显着的加速。

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