Completely blind image quality assessment via image gray-scale fluctuations and fractal dimension analysis

Yang Xichen; Sun Quansen; Wang Tianshu

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Completely blind image quality assessment via image gray-scale fluctuations and fractal dimension analysis

机译：通过图像灰度波动和分形维数分析完全盲目图像质量评估

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State-of-the-art no-reference image quality assessment methods usually learn to evaluate image quality by regression from the human subjective scores of a training set. Their dependence on the regression algorithm and human subjective scores may limit the practical application of such methods. In this paper, we propose a completely blind image quality assessment method that is highly unsupervised and training free. We first use a specific image primitive to analyze the image gray-scale fluctuation and use this result as one of the image quality assessment features. The box-counting method is then used to evaluate the image fractal dimension, and the result is used as the other feature. Finally, the two features are combined together, and a formula is introduced to calculate a comprehensive image quality feature, which is used to measure the image quality. Experimental results on four open databases show that the newly proposed method correlates well with the human subjective judgments of diversely distorted images. (C) 2018 Optical Society of America

机译：最先进的无参考图像质量评估方法通常学会通过从训练集的人类主观评分的回归评估图像质量。它们对回归算法和人类主观评分的依赖可以限制这些方法的实际应用。在本文中，我们提出了一种完全盲目的图像质量评估方法，非常无人监督和训练。我们首先使用特定的图像原语来分析图像灰度波动并使用此结果作为图像质量评估功能之一。然后使用盒子计数方法来评估图像分形尺寸，结果用作其他特征。最后，两个特征组合在一起，并引入公式以计算用于测量图像质量的综合图像质量特征。四个开放数据库的实验结果表明，新提出的方法与人类主观判断的多种扭曲图像的主观判断相关。（c）2018年光学学会

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《Applied optics》 |2018年第12期|共13页
作者
Yang Xichen; Sun Quansen; Wang Tianshu;
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Nanjing Univ Sci &

Technol Sch Comp Sci &

Engn Xiaolingwei 200 Nanjing 210094 Jiangsu Peoples R China;

Nanjing Univ Sci &

Technol Sch Comp Sci &

Engn Xiaolingwei 200 Nanjing 210094 Jiangsu Peoples R China;

Nanjing Univ Sci &

Technol Sch Comp Sci &

Engn Xiaolingwei 200 Nanjing 210094 Jiangsu Peoples R China;

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正文语种 eng
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Completely blind image quality assessment via image gray-scale fluctuations and fractal dimension analysis

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