By simulating the human visual nerve′ s processing mechanism of receiving visual information to form surface perceptio n,this paper proposes a full-reference stereoscopic image quality assessment (I QA) method based on the laminar cortical model.In the proposed method,we firstly analyze the process of forming the surface perception in the human brain,and propose a laminar cortical model for quality prediction.Then,fea tures are extracted by getting the response output of each layer based on the model.Finally,a regression model is learned to map human subjective scores and features by machine learning algorithm to predict the qual ity of a stereoscopic image. Based on the model,experimental results on three public databases demonstrate t hat the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation c oefficient (SROCC) of the proposed method are higher than 0.91in symmetric database and are higher than 0.93in asymmetric databases .The proposed algorithm achieves higher agreement with subjective assessment compared with the most related existing methods,making it better suited for the evaluation and optimization of stereoscopic image.