NoReference Stereoscopic Image Quality Assessment Based On ShuffleConvolutional Neural Network With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has been gaining great attention. In this work, to find a better SIQA method conforming to the perceptual characteristics of our brain, we propose a twochannel convolutional neural network (CNN) based on shuffle unit, which is called SCNN, for noreference SIQA. The shuffle unit is used to mix up the features extracted from the left and right views to complete information communication between the two views. Different from other SIQA methods, the four shuffle units among proposed model achieve the multiple binocular fusions while processing the left and right views. Moreover, the Shuffle v2 block before the global pooling layer further improves the accuracy of SCNN. In addition, it is worth noting that we employ decorrelated batch normalization (DBN) to obtain the better generalization ability. Experimental results demonstrate that the proposed model outperforms the stateoftheart noreference SIQA methods.

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.