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A visual model for quality driven refinement of global illumination

Published: 16 September 2017 Publication History

Abstract

When rendering complex scenes using path-tracing methods, long processing times are required to calculate a sufficient number of samples for high quality results. In this paper, we propose a new method for priority sampling in path-tracing that exploits restrictions of the human visual system by recognizing whether an error is perceivable or not. We use the stationary wavelet transformation to efficiently calculate noise-contrasts in the image based on the standard error of the mean. We then use the Contrast Sensitivity Function and Contrast Masking of the Human Visual System to detect if an error is perceivable for any given pixel in the output image. Errors that can not be detected by a human observer are then ignored in further sampling steps, reducing the amount of samples calculated while producing the same perceived quality. This approach leads to a drastic reduction in the total number of samples required and therefore in total rendering time.

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cover image ACM Conferences
SAP '17: Proceedings of the ACM Symposium on Applied Perception
September 2017
101 pages
ISBN:9781450351485
DOI:10.1145/3119881
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 September 2017

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Author Tags

  1. global illumination
  2. massively parallel algorithms
  3. visual model

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SAP '17
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SAP '17: ACM Symposium on Applied Perception 2017
September 16 - 17, 2017
Cottbus, Germany

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