To solve the problem in assessment algorithms of traditional three-dimensional visual comfort

which generally require a large amount of training data with subjective mean opinion scores to train a regression model

we propose a new visual comfort assessment model via multiple kernel boosting (MKL) method. First

considering the fact that humans tend to conduct a preference judgment between two stereoscopic images in terms of visual comfort

we select some representative stereoscopic images to generate preference stereoscopic image pairs (PSIPs) and construct a PSIP training set with a preference label set. Second

we extract multiple disparity statistics and feature type derived by estimating neural activity

associated with horizontal disparity processing. Then

a preference classification model is trained on the basis of the MKL method by taking the vector of the aforementioned differential features and corresponding preference label of each PSIP as input. Besides

a mapping strategy between classification probability and final predictive visual comfort is analyzed. Experimental results demonstrate that the proposed method can obtain a Pearson linear correlation coefficient (PLCC) larger than 0.84 and Spearman's rank correlation coefficient (SRCC) larger than 0.80

which are superior to those of other existing representative regression methods; and the cross-database testing further validates that it can achieve better PLCC and SRCC performance compared with support vector regression models. Compared with traditional regression algorithms

the proposed method performs better in predicting visual comfort accurately.