Stereoscopic image quality assessment is an effective way to evaluate the performance of stereoscopic video system,and objective stereoscopic image quality assessment consistent with subj ective perception is still a great challenge in image quality assessment.In this paper,an objective quality assessment meth od for stereoscopic image based on sparse representation is proposed.The proposed method is composed of two stages :training and testing.In the training stage,sparse dictionaries on different frequency channels are learned from the training images.In the testing stage,sparse features for all images are extracted based on the learnt sparse dictionaries,and sparse feature similarities between the reference and distorted images are calculated for each view.In addition,the gain of each channel and weights of two views are computed to model the binocular physiologic al behaviors.Finally,by fusing the gain and weights,the objective quality score is obtained.Experimental resu lts show that the proposed metric can achieve better consistency with subjective assessment,which indicates that the metric can predict human visual perception very well.