Abstract
Stereoscopic image quality assessment (SIQA) is to predict the human perception quality of stereoscopic image pairs, which is more challenging than previous 2D image quality assessment due to the complicated binocular vision mechanism in the human visual system (HVS). Recently witnessed the significant progress of biotechnology and motivated by the deeper research on the HVS, we take a step to bridge the gap between HVS and SIQA by generalizing the optic chiasm algorithm and introducing biological vision fusion mechanism in our work. Firstly, a network structure is proposed in our work, which consists of an optic chiasm module for binocular information exchange and a multi-scale feature extraction module for binocular information fusion. Secondly, a mutual-perception attention fusion module is designed for simulating binocular fusion. In addition, we come up with an innovative data enhancement method. Experimental results show that the image quality assessment score obtained by the network is more consistent with human perception.
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