No-Reference Stereoscopic Image Quality Assessment Based On Shuffle-Convolutional Neural Network

Abstract

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 two-channel convolutional neural network (CNN) based on shuffle unit, which is called SCNN, for no-reference 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 state-of-the-art no-reference SIQA methods.