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No-reference quality assessment for contrast-distorted images based on multifaceted statistical representation of structure

Zhou, Yu, Li, Leida, Zhu, Hancheng, Liu, Hantao, Wang, Shiqi and Zhao, Yao 2019. No-reference quality assessment for contrast-distorted images based on multifaceted statistical representation of structure. Journal of Visual Communication and Image Representation 60 , pp. 158-169. 10.1016/j.jvcir.2019.02.028

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Abstract

In many real-world applications, images are prone to be degraded by contrast distortions during image acquisition. Quality assessment for contrast-distorted images is vital for benchmarking and optimizing the contrast-enhancement algorithms. To this end, this paper proposes a no-reference quality metric for contrast-distorted images based on Multifaceted Statistical representation of Structure (MSS). The “Multifaceted” has two meanings, namely (1) not only the luminance information, but also the chromatic information is used for structure representation. This is inspired by the fact that the chromatic information on the one hand affects the perception of image quality as well, and on the other hand it changes along with the contrast distortions. Therefore, the chromatic information should be integrated with the luminance information for quality assessment of contrast-distorted images, a fact most existing quality metrics overlook; (2) regarding structure representation, three aspects, i.e. spatial intensity, spatial distribution, and orientation of structures are calculated, which is enlightened by the fact that the human visual system (HVS) is sensitive to the three aspects of structures. Specifically, the image is first transformed from RGB to the S-CIELAB color space to obtain a representation that is more consistent with the characteristics of the HVS, as well as to separate the chromatic information from the luminance. Then the statistical structural features are extracted from both luminance and chromatic channels. Finally, the back propagation (BP) neural network is adopted to train a quality prediction model. Experimental results conducted on four public contrast-distorted image databases demonstrate the superiority of the proposed method to the relevant state-of-the-arts.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 1047-3203
Date of Acceptance: 20 February 2019
Last Modified: 26 Feb 2019 11:46
URI: http://orca.cf.ac.uk/id/eprint/119842

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