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Analysis of reported error in Monte Carlo rendered images

Joss Whittle, Mark Jones Orcid Logo, Rafał Mantiuk

The Visual Computer, Volume: 33, Issue: 6-8, Pages: 705 - 713

Swansea University Author: Mark Jones Orcid Logo

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DOI (Published version): 10.1007/s00371-017-1384-7

Abstract

Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying amounts of computation. The use of a gold-standard, reference image, or groun...

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Published in: The Visual Computer
ISSN: 0178-2789 1432-2315
Published: Springer Science and Business Media LLC 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa33015
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spelling 2020-07-30T08:56:48.4505913 v2 33015 2017-04-22 Analysis of reported error in Monte Carlo rendered images 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2017-04-22 MACS Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying amounts of computation. The use of a gold-standard, reference image, or ground truth (GT) is a common method to provide a baseline with which to compare experimental results. We show that if not chosen carefully the reference image can skew results leading to significant misreporting of error. We present an analysis of error in Monte Carlo rendered images and discuss practices to avoid or be aware of when designing an experiment. Journal Article The Visual Computer 33 6-8 705 713 Springer Science and Business Media LLC 0178-2789 1432-2315 Image quality assessment; Error metric; Monte Carlo rendering 1 6 2017 2017-06-01 10.1007/s00371-017-1384-7 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University RCUK 2020-07-30T08:56:48.4505913 2017-04-22T13:52:41.0880562 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Joss Whittle 1 Mark Jones 0000-0001-8991-1190 2 Rafał Mantiuk 3 33015__17807__aa1be2ecadb745b98021fe2efab713e8.pdf 2017_MC_Errorv2.pdf 2020-07-30T08:54:51.5050046 Output 2447814 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title Analysis of reported error in Monte Carlo rendered images
spellingShingle Analysis of reported error in Monte Carlo rendered images
Mark Jones
title_short Analysis of reported error in Monte Carlo rendered images
title_full Analysis of reported error in Monte Carlo rendered images
title_fullStr Analysis of reported error in Monte Carlo rendered images
title_full_unstemmed Analysis of reported error in Monte Carlo rendered images
title_sort Analysis of reported error in Monte Carlo rendered images
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
author Mark Jones
author2 Joss Whittle
Mark Jones
Rafał Mantiuk
format Journal article
container_title The Visual Computer
container_volume 33
container_issue 6-8
container_start_page 705
publishDate 2017
institution Swansea University
issn 0178-2789
1432-2315
doi_str_mv 10.1007/s00371-017-1384-7
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying amounts of computation. The use of a gold-standard, reference image, or ground truth (GT) is a common method to provide a baseline with which to compare experimental results. We show that if not chosen carefully the reference image can skew results leading to significant misreporting of error. We present an analysis of error in Monte Carlo rendered images and discuss practices to avoid or be aware of when designing an experiment.
published_date 2017-06-01T11:12:28Z
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score 11.059316

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