Abstract: Assessment of stereoscopic video stabilization is an effective way to evaluate the performance of stereoscopic video stabilization algorithm. For the deficiency of stereoscopic video stabilization assessment method, a novel non-reference assessment scheme is proposed joint visual comfort. The variation of motion stability and visual comfort are employed to represent variation characteristics between stereoscopic video and the stabilized video, and a support vector regression model is trained for stereoscopic video stabilization assessment, combining with subjective assessment. The regression model get ability that can correctly assess stereo video stabilization algorithm’s product by learning the map between variation characteristics and subjective assessment. 55 simulation videos were collected to train the model. The experiments on 10 real videos demonstrate that the proposed schema has good stability, the model is convergent with the train video’s number up to 180. The result’s correlation between model and human subjective assessment is 93%, suggests the model can be used for object assessment of stereoscopic video stabilization.