Asymmetric two-stream network for screen content image quality assessment based on region features

Abstract

With the latest popularization of remote desktop software and online work, screen content images (SCIs) are increasingly being contacted by people. Many image quality assessment (IQA) methods based on deep learning have been proposed for SCIs. However, most IQA methods for SCIs pay less attention to regional characteristics. An asymmetric two-stream network based on region features is proposed for SCI objective quality assessment, in which features are derived from a channel separation feature extraction (CSFE) sub-network and a spatial analysis feature extraction (SAFE) sub-network. In the CSFE sub-network, three channel separation (CS) modules are utilized to strengthen the ability for capturing deepthwise features in the pictorial regions. In the SAFE sub-network, two spatial analysis (SA) module are applied to focus on the structural information in the spatial domain for the textual regions. Two asymmetric sub-networks can efficiently extract the corresponding region features by separately considering the pictorial and textual regions of SCIs. In addition, an adaptive global weighting strategy is introduced to estimate the quality for SCIs. The experimental results on the screen image quality assessment database show that the proposed model is superior to the other state-of-the-art SCI quality assessment methods.