Staged-Learning: Assessing the Quality of Screen Content Images from Distortion Information

Abstract

The small volume of the existing screen content images (SCIs) database with human ratings restricts the training processes of no-reference (NR) image quality assessment models based on traditional machine learning and deep learning. In this letter, we propose an NR model called the multi-task distortion-learning network to jointly analyse the distortion types and distortion degree of SCIs to be the prior knowledge for predicting the SCIs quality. Specifically, we first generate sufficient distorted SCIs labelled with the distortion type and degree, which does not need much effort to conduct subjective scoring experiments. Then, relying on these data, we pre-train a multi-task learning network to obtain strong prior knowledge about assessing the image quality. Finally, we further jointly train a quality assessment network with an attention module that simulates the mechanism of processing visual signals in the human eyes. The experimental results on the public SCIs databases show that the proposed model is competitive against other state-of-art approaches and achieves better consistency with the human vision system.