Visual quality prediction on distorted stereoscopic images
Ching-Ti Lin, Tsung-Jung Liu, Kuan-Hsien Liu · 2017
We propose a no-reference 3D image quality assessment (IQA) model that can automatically evaluate stereoscopic images. First, the model extracts statistical features from 2D single-view images (i.e., the stereopair) and their pseudo-disparity (i.e., absolute difference) map. Then the features are used to train an IQA model to predict the image quality score by the regression module of support vector machine (SVM). The model we proposed is tested on LIVE 3D Image Quality Database Phase I, which contains only symmetric-distorted stereoscopic images, and LIVE 3D Image Quality Database Phase II, which contains both symmetric-distorted and asymmetric-distorted stereoscopic images. The experimental results on both LIVE 3D Database Phase I and II show that our proposed model leads to significantly improved performance on quality prediction of stereoscopic images.