Naturalization Module in Neural Networks for Screen Content Image Quality Assessment

Deep learning approaches have demonstrated success in no-reference image quality assessment tasks. However, due to the specific properties of screen content images (SCIs), deep neural networks for SCI quality assessment are not as optimal as those designed for images depicting natural scenes. In order to tackle this discrepancy, a “naturalization” module composed of an upsampling layer and a convolutional layer is proposed to transform SCIs to have characteristics more similar to that of natural images. In addition, a new deep learning model architecture along with data augmentation techniques tailored to SCIs are implemented. The performance of the proposed approach is evaluated on the Screen Image Quality Assessment Database and Screen Content Image Database, and has shown to have superior performance to state-of-the-art methods in predicting the perceptual quality of SCIs.