Title: Improved Stochastic Texture Filtering Through Sample Reuse Show Chinese title

Title: 通过样本重用来改进随机纹理过滤

Abstract: Stochastic texture filtering (STF) has re-emerged as a technique that can bring down the cost of texture filtering of advanced texture compression methods, e.g., neural texture compression. However, during texture magnification, the swapped order of filtering and shading with STF can result in aliasing. The inability to smoothly interpolate material properties stored in textures, such as surface normals, leads to potentially undesirable appearance changes. We present a novel method to improve the quality of stochastically-filtered magnified textures and reduce the image difference compared to traditional texture filtering. When textures are magnified, nearby pixels filter similar sets of texels and we introduce techniques for sharing texel values among pixels with only a small increase in cost (0.04--0.14~ms per frame). We propose an improvement to weighted importance sampling that guarantees that our method never increases error beyond single-sample stochastic texture filtering. Under high magnification, our method has >10 dB higher PSNR than single-sample STF. Our results show greatly improved image quality both with and without spatiotemporal denoising. Show Chinese abstract

Abstract: 随机纹理滤波（STF）已成为一种可以降低高级纹理压缩方法（例如神经纹理压缩）纹理滤波成本的技术。然而，在纹理放大过程中，STF中过滤和着色的交换顺序可能导致混叠现象。无法平滑插值存储在纹理中的材质属性（例如表面法线）会导致潜在的不理想外观变化。 我们提出了一种新颖的方法来提高随机滤波放大纹理的质量，并减少与传统纹理滤波相比的图像差异。当纹理被放大时，附近的像素会过滤相似的纹理元素集合，我们引入了技术手段在像素之间共享纹理元素值，而仅增加少量成本（每帧0.04-0.14毫秒）。我们还改进了加权重要性采样，以确保我们的方法不会使误差超过单样本随机纹理滤波。 在高倍放大情况下，我们的方法比单样本STF高出>10 dB的PSNR。我们的结果显示，无论是否使用时空去噪，图像质量都得到了极大的提升。