Adaptive image space shading for motion and defocus blur

We present a novel anisotropic sampling algorithm for image space shading which builds upon recent advancements in decoupled sampling for stochastic rasterization pipelines. First, we analyze the frequency content of a pixel in the presence of motion and defocus blur. We use this analysis to derive bounds for the spectrum of a surface defined over a two-dimensional and motion-aligned shading space. Second, we present a simple algorithm that uses the new frequency bounds to reduce the number of shaded quads and the size of decoupling cache respectively by 2X and 16X, while largely preserving image detail and minimizing additional aliasing.