Deep neural network based distortion parameter estimation for blind quality measurement of stereoscopic images

Stereoscopic/3D image quality measurement (SIQM) has emerged as an active and important research branch in image processing/computer vision field. Existing methods for blind/no-reference SIQM often train machine-learning models on degraded stereoscopic images for which human subjective quality ratings have been obtained, and they are thus constrained by the fact that only a limited number of 3D image quality datasets currently exist. Although methods have been proposed to overcome this restriction by predicting distortion parameters rather than quality scores, the approach is still limited to the time-consuming, hand-crafted features extracted to train the corresponding classification/regression models as well as the rather complicated binocular fusion/rivalry models used to predict the cyclopean view. In this paper, we explore the use of deep learning to predict distortion parameters, giving rise to a more efficient opinion-unaware SIQM technique. Specifically, a deep fusion-and-excitation network which takes into account the multiple-distortion interactions is proposed to perform distortion parameter estimation, thus avoiding hand-crafted features by using convolution layers while simultaneously accelerating the algorithm by using the GPU. Moreover, we measure distortion parameter values of the cyclopean view by using support vector regression models which are trained on the data obtained from a newly-designed subjective test. In this way, the potential errors in computing the disparity map and cyclopean view can be prevented, leading to a more rapid and precise 3D-vision distortion parameter estimation. Experimental results tested on various 3D image quality datasets demonstrate that our proposed method, in most cases, offers improved predictive performance over existing state-of-the-art methods.