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Analysis of reported error in Monte Carlo rendered images / Joss Whittle; Mark W. Jones; Rafał Mantiuk

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

Swansea University Author: Jones, Mark

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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: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa33015
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spelling 2019-04-01T13:45:22Z v2 33015 2017-04-22 Analysis of reported error in Monte Carlo rendered images Mark Jones Mark Jones true 0000-0001-8991-1190 false 2e1030b6e14fc9debd5d5ae7cc335562 dda0c29127c698255a4c2b822dd94125 uiPdnV+XNibOpUxFjI3lXQgr5y2nBRz3haj4DmVVDsQ= 2017-04-22 SCS 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 0178-2789 1432-2315 3 6 2017 2017-06-03 10.1007/s00371-017-1384-7 College of Science Computer Science CSCI SCS Visual Computing RCUK None 2019-04-01T13:45:22Z 2017-04-22T13:52:41Z College of Science Computer Science Joss Whittle 1 Mark W. Jones 2 Rafał Mantiuk 3 0033015-22042017140012.pdf 2017_MC_Error.pdf 2017-04-22T14:00:12Z Output 1264249 application/pdf AM true Updated Copyright 19/05/2017 2017-05-13T00:00:00 true eng 0033015-13052017200622.pdf 2017_MC_Errorv2.pdf 2017-05-13T20:06:22Z Output 2298627 application/pdf VoR true Updated Copyright 19/05/2017 2017-05-13T00:00:00 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. true eng
title Analysis of reported error in Monte Carlo rendered images
spellingShingle Analysis of reported error in Monte Carlo rendered images
Jones, Mark
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_***_Jones, Mark
author Jones, Mark
author2 Joss Whittle
Mark W. 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
college_str College of Science
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hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
document_store_str 1
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researchgroup_str Visual Computing
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 2017-06-03T21:38:11Z
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