Distortion recognition for image quality assessment with convolutional neural network

The past decades have witnessed a growing development of image quality assessment (IQA). However, there is still much room to improve the IQA performance, especially for the no reference IQA problem. In this paper, a convolutional neural network (CNN) based approach is designed to predict the distortion type of an image and assess its quality without original reference. With the proposed CNN approach, an image is divided into patches and a selective weighted average method is designed to fuse the image quality score from the image patches with the aid of recognized distortion type. Experimental results on the benchmark LIVE image quality database verify the effectiveness of the proposed CNN based approach as compared with several state-of-the-art full reference and no reference image quality metrics.

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