Weighted ensemble learning prediction for blind symmetrically distorted stereoscopic images

In recent years, deep learning has been largely applied to 2D image quality assessment (2D-IQA) but rarely to 3D image quality assessment (3D-IQA). In this paper, a new method for blind symmetrically distorted stereoscopic images quality assessment is proposed to utilize multiple features fusion in deep network to assess the left view and right view as an integration with no extra cost. According to binocular rivalry, a weighted ensemble learning network is developed for learning energy of dominant eye. We integrate these two networks into a full end-to-end network called a Weighted Ensemble Deep Quality Network (WEDQN). Our experimental results can demonstrate that the proposed method leads to significant improved quality prediction of symmetrically distorted stereoscopic images.