English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Perceptual Error Optimization for Monte Carlo Rendering

MPS-Authors
/persons/resource/persons256055

Chizhov,  Vassillen
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45095

Myszkowski,  Karol
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons225790

Singh,  Gurprit
Computer Graphics, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)

arXiv:2012.02344.pdf
(Preprint), 52MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Chizhov, V., Georgiev, I., Myszkowski, K., & Singh, G. (2020). Perceptual Error Optimization for Monte Carlo Rendering. Retrieved from https://arxiv.org/abs/2012.02344.


Cite as: http://hdl.handle.net/21.11116/0000-0007-CEB7-3
Abstract
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.