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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3S3EAQ2
Repositorysid.inpe.br/sibgrapi/2018/10.17.19.12
Last Update2018:10.17.19.12.00 (UTC) pedrogarcia@ieee.org
Metadata Repositorysid.inpe.br/sibgrapi/2018/10.17.19.12.01
Metadata Last Update2022:05.18.22.18.33 (UTC) administrator
Citation KeyFreitasFari:2018:UsTeMe
TitleUsing Texture Measures for Visual Quality Assessment
FormatOn-line
Year2018
Access Date2024, Apr. 29
Number of Files1
Size2982 KiB
2. Context
Author1 Freitas, Pedro Garcia
2 Farias, Mylène C. Q.
Affiliation1 University of Brasília
2 University of Brasília
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresspedrogarcia@ieee.org
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2018-10-17 19:12:01 :: pedrogarcia@ieee.org -> administrator ::
2022-05-18 22:18:33 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsVisual quality
objective metrics
no-reference image quality assessment
video quality assessment
AbstractThe automatic quality assessment of images and videos is a crucial problem for a wide range of applications in the fields of computer vision and multimedia processing. For instance, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, a great research effort has been made to develop a visual quality assessment (VQA) methods that are able to automatically estimate quality. However, VQA still faces several challenges. In the case of images, most of the proposed methods are complex and require a reference (pristine image) to estimate the quality, which limits their use in several multimedia applications. For videos, the current state-of-the-art methods still perform worse than the methods designed for images, both in terms of prediction accuracy and computational complexity. In this work, we proposed a set of methods to estimate visual quality using texture descriptors and machine learning. Starting from the premise that visual impairments alter image and video texture statistics, we propose a framework that use these descriptors to produce new quality assessment methods, including no-reference (blind) and full-reference quality metrics. Experimental results indicate that the proposed metrics present a good performance when tested on several benchmark image and video quality databases, outperforming current state-of-the-art metrics.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2018 > Using Texture Measures...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3S3EAQ2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3S3EAQ2
Languageen
Target Filewtd-manuscript-CR.pdf
User Grouppedrogarcia@ieee.org
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 9
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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