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@InProceedings{FreitasFari:2017:PeViSe,
               author = "Freitas, Pedro Garcia and Farias, Myl{\`e}ne C. Q.",
          affiliation = "{University of Bras{\'{\i}}lia} and {University of 
                         Bras{\'{\i}}lia}",
                title = "On the Performance of Visual Semantics for Improving Texture-based 
                         Blind Image Quality Assessment",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image Quality Assessment, Opposite Color Local Binary Patterns, 
                         ImageNet, Deep Learning, Semantic Features.",
             abstract = "Blind 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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "sibgrapi2017-cameraready-v2.pdf",
        urlaccessdate = "2021, Jan. 26"
}


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