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《Optical Technique》 2018-05
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No-reference multiply distorted images quality assessment based on convolutional neural network

WU Lixiu;SANG Qingbing;School of Internet of Things Engineering,Jiangnan University;  
Most of the existing algorithms of objective image quality assessment are only designed for a single type of distortion and they are not applicable for mixed distortion.Among these methods,most are traditional machine learning and few are deep learning.A no-reference image quality assessment method based on phase congruency and convolutional neural networks is proposed to evaluate the mixed distorted images.The input images are divided into patches and processed by phase congruency transformation,convolutional neural networks are used to train and predict quality scores of images.The convolutional network structure consists of 4 layers of convolutions,3 layers of maximum pooling,and 2 fully connected layers.The experimental results on the Live multiply distortion quality evaluation database show that the proposed method has a good consistency between the image quality and the subjective quality score.
【Fund】: 国家自然科学基金(61673194 61672265);; 江苏省产学研前瞻性联合研究项目(BY2016022-17/001);; 江苏省自然科学基金项目(BK20171142)
【CateGory Index】: TP391.41;TP181
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