In this paper, we proposed an effective 3-D image-quality assessment method based on an adaptive cyclopean image by using ensemble learning. Our cyclopean image is not only suitable for a symmetrical distortion image, but also especially suitable for an asymmetrical distortion image. This adaptivity of our cyclopean image can be attributed to the consideration of gain control and gain enhancement in a binocular rivalry visual mechanism. In addition, we use a salient map to modify our cyclopean image to let the salient area of our cyclopean become more attractive. As a result, we can get better results. To remove redundant information out from our cyclopean, the sparse representation is applied to extract essential features. Finally, to get better regression accuracy on extracted feature, we use ensemble learning to get the final quality score of a stereoscopic image. The ensemble learner can improve the regression accuracy by 2% than a single learner. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods on two publicly available stereoscopic image-quality assessment databases LIVE I and LIVE II.