Blue-noise Dithered Sampling

Iliyan Georgiev, Marcos Fajardo
SIGGRAPH 2016 (talk)

Our method uses blue-noise dither masks tiled over the image to correlate the samples between pixels and thus minimize the low-frequency content in the distribution of the estimation error. Without actually reducing the amount of error, this correlation produces images with higher visual fidelity than traditional random pixel decorrelation, especially when using a small number of samples per pixel (spp).


The visual fidelity of a Monte Carlo rendered image depends not only on the magnitude of the pixel estimation error but also on its distribution over the image. To this end, state-of-the-art methods use high-quality stratified sampling patterns, which are randomly scrambled or shifted to decorrelate the individual pixel estimates.

While the white-noise image error distribution produced by random pixel decorrelation is eye-pleasing, it is far from being perceptually optimal. We show that visual fidelity can be significantly improved by instead correlating the pixel estimates in a way that minimizes the low-frequency content in the output noise. Inspired by digital halftoning, our blue-noise dithered sampling can produce substantially more faithful images, especially at low sampling rates.

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BibTeX reference

  title = {Blue-noise Dithered Sampling},
  author = {Iliyan Georgiev and Marcos Fajardo},
  journal = {ACM SIGGRAPH 2016 Talks},
  year = {2016},
  ISBN = {978-1-4503-4282-7},
  DOI = {10.1145/2897839.2927430}