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Image Quality Assessment Based on Non-local High Dimensional Feature Analysis |
DING Yong LI Nan |
(Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China) |
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Abstract Traditionally, low dimensional features for partial information are extracted to analyze image quality. Though high dimensional features are difficult to be analyzed, they contain more information to fully analyze image quality. On this condition, this paper proposes an image quality assessment method based on non-local high dimensional feature analysis after optimized data sampling. Firstly, image data is filtered by using block matching method and dimensionally reduced by Principal Component Analysis (PCA). Secondly, Kernel Independent Component Analysis (KICA) is applied to extract high dimensional features. The features are finally synthesized to evaluate image quality based on natural image statistics. The experimental results show that the proposed method keeps accordance with human objective perception.
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Received: 17 December 2015
Published: 12 June 2016
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Fund: Items: The National 863 Program of China (2015AA016704c), Zhejiang Provincial Natural Science Foundation (LY14F020028) |
Corresponding Authors:
LI Nan
E-mail: linan@vlsi.zju.edu.cn
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