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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PF286B
Repositorysid.inpe.br/sibgrapi/2017/08.16.15.06
Last Update2017:08.23.13.45.57 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.16.15.06.49
Metadata Last Update2022:06.14.00.08.42 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.50
Citation KeyFreitasFari:2017:PeViSe
TitleOn the Performance of Visual Semantics for Improving Texture-based Blind Image Quality Assessment
FormatOn-line
Year2017
Access Date2024, Oct. 08
Number of Files1
Size12718 KiB
2. Context
Author1 Freitas, Pedro Garcia
2 Farias, Mylène C. Q.
Affiliation1 University of Brasília
2 University of Brasília
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresssawp@sawp.com.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-23 13:45:57 :: sawp@sawp.com.br -> administrator :: 2017
2022-06-14 00:08:42 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsImage Quality Assessment
Opposite Color Local Binary Patterns
ImageNet
Deep Learning
Semantic Features
AbstractBlind image quality assessment (BIQA) methods aim to estimate the quality of a given test image without referring to the corresponding reference (original) image. Most BIQA methods use visual sensitivity models, which take into consideration intrinsic image characteristics (e.g. contrast, luminance, and texture) to identify degradations and estimate quality. For example, texture-based BIQA methods are based on the assumption that visual impairments (degradations) alter the characteristics of the image textures and, therefore, their statistics. Although these methods have been are known to provide an acceptable performance, they do not take into account the semantic information of the image. In this paper, we propose a BIQA method that estimates quality using texture characteristics and semantic information. The texture characteristics are obtained using the Opponent Color Local Binary Pattern (OCL) operator. The semantic information is obtained by estimating the probability distribution of the scene characteristics. A random forest regression algorithm is used to map semantic and texture-based features into a quality score. Results obtained testing the proposed BIQA method on several public databases show the method has a good accuracy on quality prediction.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > On the Performance...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > On the Performance...
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sibgrapi2017-cameraready-v1.pdf 16/08/2017 12:06 12.4 MiB
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agreement.html 16/08/2017 12:06 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PF286B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF286B
Languageen
Target Filesibgrapi2017-cameraready-v2.pdf
User Groupsawp@sawp.com.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 52
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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