Stereoscopic Image Quality Assessment Weighted Guidance by Disparity Map Using Convolutional Neural Network In this paper, we propose a new twocolumn dense Convolutional Neural Network (CNN) for stereoscopic image quality assessment. The input of one column is the cyclopean image which conforms to the binocular combination and rival mechanism in our brain. The input of other column is the disparity map which provides some compensation information for the cyclopean image. More importantly, we employ the features of disparity map to guide and weight the feature maps obtained from the cyclopean image, which is implemented by modifying the structure of Squeeze and Excitation block. This weighting strategy recalibrates the importance of feature maps extracted from cyclopean image. At the end of CNN, we combine the outputs from the twocolumn through xe2x80x99Concatxe2x80x99, and then process them to get the final quality score of the stereoscopic image. Experimental results demonstrate that the proposed method can achieve high consistent alignment with subjective assessment.

NoReference 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, CNNbased SIQA methods emerge one after another. However, current methods only consider a single scale or resolution, and some CNNbased methods directly take left and right views as an input of the network ignoring the visual fusion mechanism. In this work, a multiscale noreference SIQA method is proposed based on dilation convolution neural network (DCNN). Different from other CNNbased 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 multiscale unit significantly can reduce computational parameters and computational complexity. Experimental results on two public databases show that the proposed model is superior to the stateoftheart noreference SIQA methods.