Realistic image synthesis involves computing high-dimensional light transport integrals which in practice are numerically estimated using Monte Carlo integration. The error of this estimation manifests itself in the image as visually displeasing aliasing or noise. To ameliorate this, we develop a theoretical framework for optimizing screen-space error distribution. Our model is flexible and works for arbitrary target error power spectra. We focus on perceptual error optimization by leveraging models of the human visual system's (HVS) point spread function (PSF) from halftoning literature. This results in a specific optimization problem whose solution distributes the error as visually pleasing blue noise in image space. We develop a set of algorithms that provide a trade-off between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using both quantitative and perceptual error metrics to support our analysis, and provide extensive supplemental material to help evaluate the perceptual improvements achieved by our methods.
翻译:现实的图像合成涉及使用蒙特卡洛集成法计算高维光传输元件,这些元件实际是用数字估计的。这种估计的错误表现在图像中,是视觉上令人不悦的别名或噪音。为了改善这一点,我们开发了一个优化屏幕-空间错误分布的理论框架。我们的模型是灵活的,用于任意目标误差能量光谱。我们侧重于通过利用半调文献中的人类视觉系统(HVS)点扩散功能模型来优化感知错误。这导致一个具体的优化问题,其解决方案将错误作为图像空间中视觉上令人愉快的蓝色噪音来传播。我们开发了一套算法,在质量和速度上进行权衡,显示与艺术先前状态相比的重大改进。我们使用定量和感知误差指标进行评估,以支持我们的分析,并提供广泛的补充材料,帮助评估我们方法的观念改进。