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Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47N4FBP
Repositorysid.inpe.br/sibgrapi/2022/09.30.00.44
Last Update2022:09.30.00.44.38 (UTC) mylene@ieee.org
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.30.00.44.38
Metadata Last Update2023:05.23.04.20.43 (UTC) administrator
Citation KeySaiggDiaCosMarFar:2022:PyFrOb
TitleA Python Framework for Objective Visual Quality Assessment
FormatOn-line
Year2022
Access Date2024, Apr. 29
Number of Files1
Size14968 KiB
2. Context
Author1 Saigg, Caio L.
2 Dias, Bruno S. S.
3 Costa, André H. M.
4 Martinez, Helard B.
5 Farias, Mylene C. Q.
Affiliation1 Universidade de Brasilia
2 Universidade de Brasilia
3 Universidade de Brasilia
4 University College Dublin
5 Universidade de Brasilia
e-Mail Addressmylene@ieee.org
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2022-09-30 00:44:38 :: mylene@ieee.org -> administrator ::
2023-05-23 04:20:43 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsvideo quality
quality of experience
image processing
AbstractThis work introduces a Quality Assessment Framework that provides researchers with the flexibility, consistency, and scalability they need to evaluate and compare quality metrics, promoting the reproducibility of results. The framework is open source (Python) and currently has 11 visual quality metrics that use 3 different libraries: Scikit-video, FFmpeg toolkit, and PyMetrikz. It can be easily expanded to include more metrics in the future and allows testing on several quality datasets. To validate it, we tested it on two datasets and compared the results with the results obtained by other authors in the literature. The results are consistent with those reported by external studies. With this evidence, new image/video metrics and datasets can be integrated into this framework. This will allow researchers to compare their methods with a wide number of quality metrics on several datasets in a fast and efficient way.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > A Python Framework...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 29/09/2022 21:44 1.6 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47N4FBP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47N4FBP
Languageen
Target File2022_sibgrapi_caio_bruno (2).pdf
User Groupmylene@ieee.org
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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