Perceptual error optimization for Monte Carlo rendering

TL;DR

This paper introduces a perception-based optimization framework for Monte Carlo rendering that reduces visual artifacts by distributing errors as blue noise, significantly enhancing image quality.

Contribution

It proposes a novel perceptual error optimization approach for Monte Carlo rendering, utilizing human perception models to produce visually pleasing noise patterns.

Findings

Significant reduction in rendering noise artifacts.

Improved image fidelity demonstrated through quantitative metrics.

Algorithms offering various quality-speed trade-offs.

Abstract

Synthesizing realistic images involves computing high-dimensional light-transport integrals. In practice, these integrals are numerically estimated via Monte Carlo integration. The error of this estimation manifests itself as conspicuous aliasing or noise. To ameliorate such artifacts and improve image fidelity, we propose a perception-oriented framework to optimize the error of Monte Carlo rendering. We leverage models based on human perception from the halftoning literature. The result is an optimization problem whose solution distributes the error as visually pleasing blue noise in image space. To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using quantitative and error metrics, and provide extensive supplemental material to demonstrate the perceptual improvements achieved by our methods.