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Image quality assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper,...
A number of image quality assessment (IQA) metrics have been designed in recent years for natural images, leading to a desire to develop IQA approaches for screen content image which is composed of textual as well as pictorial regions and exhibits different visual characteristics from the natural image. In this work, a no reference IQA metric based on convolutional neural network (CNN) is proposed...
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,...
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