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Open AccessArticle

Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment

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Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, china
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School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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Electronic Information School, Wuhan University, Wuhan 430072, China
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Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 10081, China
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School of Computer Science, Wuhan University, Wuhan 430072, China
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Author to whom correspondence should be addressed.
Entropy 2018, 20(12), 947; https://doi.org/10.3390/e20120947
Received: 21 November 2018 / Revised: 6 December 2018 / Accepted: 8 December 2018 / Published: 10 December 2018
Image quality assessment (IQA) is a fundamental problem in image processing that aims to measure the objective quality of a distorted image. Traditional full-reference (FR) IQA methods use fixed-size sliding windows to obtain structure information but ignore the variable spatial configuration information. In order to better measure the multi-scale objects, we propose a novel IQA method, named RSEI, based on the perspective of the variable receptive field and information entropy. First, we find that consistence relationship exists between the information fidelity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via rectangular-normalized superpixel segmentation. Then the weights of each image patches are adaptively calculated via their information volume. We verify the effectiveness of RSEI by applying it to data from the TID2008 database and denoise algorithms. Experiments show that RSEI outperforms some state-of-the-art IQA algorithms, including visual information fidelity (VIF) and weighted average deep image quality measure (WaDIQaM). View Full-Text
Keywords: image quality assessment; mutual information; superpixel segmentation image quality assessment; mutual information; superpixel segmentation
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Lu, T.; Wang, J.; Zhou, H.; Jiang, J.; Ma, J.; Wang, Z. Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment. Entropy 2018, 20, 947.

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