@InProceedings{FreitasFari:2018:UsTeMe,
author = "Freitas, Pedro Garcia and Farias, Myl{\`e}ne C. Q.",
affiliation = "{University of Bras{\'{\i}}lia} and {University of
Bras{\'{\i}}lia}",
title = "Using Texture Measures for Visual Quality Assessment",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Visual quality, objective metrics, no-reference image quality
assessment, video quality assessment.",
abstract = "The 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.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
language = "en",
ibi = "8JMKD3MGPAW/3S3EAQ2",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S3EAQ2",
targetfile = "wtd-manuscript-CR.pdf",
urlaccessdate = "2024, Apr. 28"
}