Stereoscopic Video Quality Assessment Based on The Twosteptraining 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 longterm fusion, competition, and processing process by simulating the longterm complex process of the whole visual pathway. And we employ a twosteptraining 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.

Stereo Image Quality Assessment Based on Sparse Binocular Fusion Convolution Neural Network In this paper, a sparse binocular fusion convolution neural network is proposed to evaluate the quality of stereo image. In order to simulate the longterm fusion and processing of the left and right views in the brain visual pathway, the network combines the two views of the stereo image four times, and the information processing is carried out through convolution together with the fusion operation. In addition, in order to overcome the computationalintensive and memoryintensive problems of convolution neural networks, a structural sparsity learning (SSL) method is used to regularize the proposed convolution neural network. The experimental results demonstrate that our proposed method performs effectively and efficiently. And the proposed method can achieve 2.0xc3x97 speedups on LIVE I database and 2.3xc3x97 speedup on LIVE II database on the basis of improved performance.