Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyFreitasFari:2017:PeViSe
TitleOn the Performance of Visual Semantics for Improving Texture-based Blind Image Quality Assessment
Access Date2021, Jan. 25
Number of Files1
Size12718 KiB
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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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-23 13:45:57 :: -> administrator :: 2017
2020-02-19 02:01:19 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
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.
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