No Cover Image

Journal article 1187 views 83 downloads

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

  • 2017_MC_Errorv2.pdf

    PDF | Version of Record

    Released under the terms of a Creative Commons Attribution License (CC-BY).

    Download (2.33MB)

Check full text

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...

Full description

Published in: The Visual Computer
ISSN: 0178-2789 1432-2315
Published: Springer Science and Business Media LLC 2017
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa33015
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2017-04-22T19:00:53Z
last_indexed 2020-07-30T12:52:18Z
id cronfa33015
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2020-07-30T08:56:48.4505913</datestamp><bib-version>v2</bib-version><id>33015</id><entry>2017-04-22</entry><title>Analysis of reported error in Monte Carlo rendered images</title><swanseaauthors><author><sid>2e1030b6e14fc9debd5d5ae7cc335562</sid><ORCID>0000-0001-8991-1190</ORCID><firstname>Mark</firstname><surname>Jones</surname><name>Mark Jones</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2017-04-22</date><deptcode>SCS</deptcode><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 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.</abstract><type>Journal Article</type><journal>The Visual Computer</journal><volume>33</volume><journalNumber>6-8</journalNumber><paginationStart>705</paginationStart><paginationEnd>713</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><issnPrint>0178-2789</issnPrint><issnElectronic>1432-2315</issnElectronic><keywords>Image quality assessment; Error metric; Monte Carlo rendering</keywords><publishedDay>1</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2017</publishedYear><publishedDate>2017-06-01</publishedDate><doi>10.1007/s00371-017-1384-7</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><degreesponsorsfunders>RCUK</degreesponsorsfunders><apcterm/><lastEdited>2020-07-30T08:56:48.4505913</lastEdited><Created>2017-04-22T13:52:41.0880562</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Joss</firstname><surname>Whittle</surname><order>1</order></author><author><firstname>Mark</firstname><surname>Jones</surname><orcid>0000-0001-8991-1190</orcid><order>2</order></author><author><firstname>Rafa&#x142;</firstname><surname>Mantiuk</surname><order>3</order></author></authors><documents><document><filename>33015__17807__aa1be2ecadb745b98021fe2efab713e8.pdf</filename><originalFilename>2017_MC_Errorv2.pdf</originalFilename><uploaded>2020-07-30T08:54:51.5050046</uploaded><type>Output</type><contentLength>2447814</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Released under the terms of a Creative Commons Attribution License (CC-BY).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
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 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 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 Computer Science COLLEGE CODE SCS 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
document_store_str 1
active_str 0
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 2017-06-01T03:40:38Z
_version_ 1763751848323317760
score 11.013148

Similar Items