Quality Assessment of Screen Content Images Based on Convolutional Neural Network with Dual Pathways

Abstract

To simulate the characteristics of perceiving things from binocular vision, a dual-pathway convolutional neural network (CNN) for quality assessment of screen content images (SCIs) is proposed. Considering the different sensitivity of retinal photoreceptor cells to RGB colors and the human visual attention mechanism, we employ a convolutional block attention module (CBAM) to weight the RGB channels and their spatial position on each channel. And 3D convolution considering inter-frame information is used to extract the correlation features between RGB channels. Moreover, because of the important role of optic chiasm in binocular vision, we design its simulation strategy in the proposed network. Furthermore, since the characteristics of multi-scale and multi-level are indispensable to perception of any objects in human visual system (HVS), a new multi-scale and multi-level feature fusion (MSMLFF) module is built to obtain perceptual features of different scales and levels. Experimental results show that the proposed method is superior to several mainstream SCIs metrics on publicly accessible databases.