Objective Stereoscopic Image Quality Assessment Model Based on Support Vector Regression

Stereoscopic image quality assessment is an effective way to evaluate the performance of stereoscopic video system. However, how to use human visual characteristics effectively is still a research focus in objective stereoscopic image quality. In this paper, combining with the stability characteristics of singular values and subjective visual characteristics of stereoscopic images, an objective stereoscopic image quality assessment model based on Support Vector Regression (SVR) is proposed. In the model, firstly, stereoscopic features are obtained by extracting singular values of left and right images. Secondly, the features are fused according to different types of distortion. Finally, the values of objective assessment are predicted by SVR. Experimental results show that, by applying the proposed model to stereoscopic test database, Persons correlation coefficient index reaches to 0.93, Ranked correlation coefficient index reaches to 0.94, Root Mean Square Error (RMSE) index approaches to 6, and outlier ratio index reaches to 0.00%, which indicate that the model is fairly good and can predict human visual perception very well.