Identity statement area
Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyFreitasFari:2018:UsTeMe
TitleUsing Texture Measures for Visual Quality Assessment
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size2982 KiB
Context area
Author1 Freitas, Pedro Garcia
2 Farias, Mylène C. Q.
Affiliation1 University of Brasília
2 University of Brasília
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-17 19:12:01 :: -> administrator ::
2020-02-20 22:06:50 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeMaster's or Doctoral Work
KeywordsVisual quality, objective metrics, no-reference image quality assessment, video quality assessment.
AbstractThe 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.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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