Methods of image quality assessment are widely used for ranking computer vision algorithms or controlling the perceptual video and streaming applications. The ever-increasing number digital images has encouraged research in this field at an accelerated pace recent decades. After appearance convolutional neural networks, many researchers have paid attention to different deep architectures devise...

Image quality assessment (IQA) methods play important roles in many applications such as image communication, reception, compression, restoration, and display. No-reference IQA metrics are required to resolve an image when there is a lack of a reference image that is required for fullreference IQA metrics. We propose a no-reference IQA method to evaluate the image quality by using the differenc...

In this paper, we quest the capability of transferring quality natural scene images to that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in widely accepted view human visual system has adapted and evolved through perception environment. Here, develop first unsupervised domain adaptation based no reference assessment method for SCIs, leveraging rich subjective ...

Image quality assessment (IQA) based on deep learning faces the overfitting problem due to limited training samples available in existing IQA databases. Transfer is a plausible solution problem, which shared features derived from large-scale Imagenet source domain could be transferred original recognition task intended task. However, and target as well their corresponding tasks are not directly...