In view of the fact that the previous saliency detection models fail to fully consider the effect of stereo visual comfort and the distribution features of disparity values, a saliency computation model considering stereo visual comfort is proposed. In the extraction of color image's saliency, the model first segments an input image into super-pixel regions by using SLIC algorithm, and merges the regions according to color similarity among adjacent regions. After that, the computation of 2D image's saliency is conducted. In the computation of depth saliency, the model first preprocesses the disparity map, and then a regional disparity contrast-based saliency analysis is applied to compute the salient region of the depth map. Finally, the stereo visual comfort factor is embedded into the fusion of the 2D saliency map and depth map to obtain a final stereoscopic saliency image. We evaluated the proposed model for stereoscopic images with various scenarios. The experimental results indicate that the proposed model outperforme existing saliency detection models, yielding an 85% precision and 78% recall rate. Moreover, the saliency region distributions fit well with the human binocular visual attention.