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J. Imaging 2018, 4(10), 114; https://doi.org/10.3390/jimaging4100114

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.
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)
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Abstract

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

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