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In most state-of-the-art non-distortion-specific no-reference image quality assessment (NDS NR-IQA) methods, the image quality is predicted by training a regression model based on examples of distorted images and their corresponding human subjective scores. However, one drawback of these approaches is the fact that they require a training phase of the regression parameters. In this paper, a non-parametric...
In this paper, a no-reference image quality assessment (NR-IQA) algorithm based on a two-stage non-parametric framework is presented. At the first stage, the type of distortion affecting the test image patches is first identified via a nearest-neighbor (NN) based classifier. Utilizing the differential mean opinion score (DMOS) values associated with the labelled patches within the identified distortion...
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