@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, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.50",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.50",
language = "en",
ibi = "8JMKD3MGPAW/3PF286B",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF286B",
targetfile = "sibgrapi2017-cameraready-v2.pdf",
urlaccessdate = "2024, Apr. 28"
}