Next Article in Journal
A Non-Structural Representation Scheme for Articulated Shapes
Next Article in Special Issue
Multivariate Statistical Approach to Image Quality Tasks
Previous Article in Journal
Phase-Contrast and Dark-Field Imaging
Previous Article in Special Issue
GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm
Open AccessArticle

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

1
Department of Computer Science, University of Brasília, Brasília 73345-010, Brazil
2
Department of Electrical Engineering, University of Brasília, Brasília 73345-010, Brazil
*
Author to whom correspondence should be addressed.
J. Imaging 2018, 4(10), 114; https://doi.org/10.3390/jimaging4100114
Received: 16 July 2018 / Revised: 23 September 2018 / Accepted: 26 September 2018 / Published: 4 October 2018
(This article belongs to the Special Issue Image Quality)
Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity. View Full-Text
Keywords: texture descriptors; random forest regression; no-reference image quality assessment; machine learning texture descriptors; random forest regression; no-reference image quality assessment; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Garcia Freitas, P.; Da Eira, L.P.; Santos, S.S.; Farias, M.C.Q.d. On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment. J. Imaging 2018, 4, 114. https://doi.org/10.3390/jimaging4100114

AMA Style

Garcia Freitas P, Da Eira LP, Santos SS, Farias MCQd. On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment. Journal of Imaging. 2018; 4(10):114. https://doi.org/10.3390/jimaging4100114

Chicago/Turabian Style

Garcia Freitas, Pedro; Da Eira, Luísa P.; Santos, Samuel S.; Farias, Mylene C.Q.d. 2018. "On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment" J. Imaging 4, no. 10: 114. https://doi.org/10.3390/jimaging4100114

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop