Stereoscopic Video Quality Assessment Based on The Two-step-training Binocular Fusion Network

In this paper, we propose a novel binocular fusion network for stereoscopic video quality assessment (SVQA). In this network, we construct a long-term fusion, competition, and processing process by simulating the long-term complex process of the whole visual pathway. And we employ a two-step-training strategy for this network, which solves the problem that the network is difficult to fit caused by using the same value to label the different quality regions and views of the same stereoscopic video. In the first step, we use the computed quality scores of different patches to train the local network, namely local regression. And then the global regression is performed by using MOS value based on the first step trained model. Besides, considering temporal information, we take spatiotemporal saliency feature flows as the inputs of the proposed network. The proposed method is tested on public stereoscopic video databases, and results show that our method outperforms any other methods.