No-Reference Stereoscopic Image Quality Assessment Based on Dilation Convolution

Over the years, with the popularization of 3D technology, the demands of accurate and efficient 3D image quality evaluation (SIQA) methods are increasing constantly. Due to the wide application of CNN, CNN-based SIQA methods emerge one after another. However, current methods only consider a single scale or resolution, and some CNN-based methods directly take left and right views as an input of the network ignoring the visual fusion mechanism. In this work, a multi-scale no-reference SIQA method is proposed based on dilation convolution neural network (DCNN). Different from other CNN-based SIQA methods, the proposed one uses dilation convolution to imitate different scale of information processing fields in the human brain. Instead of left or right image, the cyclopean image generated by a new method is used as the input of the network. Moreover, the proposed multi-scale unit significantly can reduce computational parameters and computational complexity. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art no-reference SIQA methods.