Perceptual error optimization for Monte Carlo animation rendering

TL;DR

This paper introduces a method to optimize pixel error distribution in Monte Carlo animation rendering by considering both spatial and temporal perceptual effects, resulting in improved visual fidelity.

Contribution

It extends existing perceptual error optimization techniques to the spatio-temporal domain and proposes a practical algorithm for animated rendering.

Findings

Improved perceptual quality in animated Monte Carlo rendering.

Effective distribution of error as blue noise in space and time.

Demonstrated benefits across various rendering configurations.

Abstract

Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that perceptual fidelity can be improved significantly by distributing pixel error as blue noise instead. Most such works have focused on static images, ignoring the temporal perceptual effects of animation display. We extend prior formulations to simultaneously consider the spatial and temporal domains, and perform an analysis to motivate a perceptually better spatio-temporal error distribution. We then propose a practical error optimization algorithm for spatio-temporal rendering and demonstrate its effectiveness in various configurations.