No-Reference Stereoscopic Image Quality Assessment Based on Local to Global Feature Regression
Sumei Li, Jianwei Xue, Yongtian Han · 2019
In this paper, we propose a two-channel deep convolutional neural network (DCNN) through local to global regression for no-reference (NR) stereoscopic images quality assessment (SIQA). Firstly, in most deep learning based methods, they use the given subjective mean opinion score (MOS) or the differential MOS (DMOS) value to adjust the network parameters. But it is unreasonable, especially for the asymmetrical distortion stereoscopic image. To alleviate the problem, we propose to use feature similarity index (FSIM) to provide pseudo labels for the left and right view respectively, named local regression, so that the left and right channel are trained better. Then, we use the given DMOS to finetune the locally trained model parameters, named global regression. So we achieve an end-to-end network to measure the stereoscopic image quality. The experimental results show that the proposed method is superior to other existing SIQA methods.