Two-stream interactive network based on local and global information for No-Reference Stereoscopic Image Quality Assessment |
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Affiliation: | 1. Lecturer with College of information, Liaoning University, Liaoning 110036, China;2. School of Computer Science, China University of Geosciences, Wuhan 430074, China;3. Department of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;4. College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China |
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Abstract: | Nowadays, stereoscopic image quality assessment (SIQA) based on convolutional neural network (CNN) has become the mainstream model of image quality assessment (IQA). Compared with the two-dimensional quality evaluation model, stereoscopic image quality evaluation is more challenging due to the effects of depth and parallax information. In this paper, we propose a two-stream interactive network model to perform quality evaluation, which can well simulate the process of human stereo visual perception. Meanwhile, we enhance the extraction of local and global features of images by asymmetric convolution kernel and interactive sub-networks of inter-layers, respectively, which can further optimize our network model. Our proposed algorithm was evaluated on four public databases. The final experimental results show that our proposed algorithm exhibits good performance not only on the whole database but also on each single distortion type. |
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Keywords: | Stereoscopic image quality evaluation Binocular fusion Asymmetric convolution kernel CNN Summation and difference channels |
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