Perceptual Error Optimization for Monte Carlo Rendering

Autor:	Karol Myszkowski, Iliyan Georgiev, Vassillen Chizhov, Gurprit Singh
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Graphics I.3.7 Computer Graphics and Computer-Aided Design Graphics (cs.GR)
Zdroj:	ACM Transactions on Graphics. 41:1-17
ISSN:	1557-7368 0730-0301
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d7e802f40beeeeb160fcd4cdf24c144 https://doi.org/10.1145/3504002 Zobrazit plný text záznamu