Frequency analysis of visual data is a fundamental step in understanding the characteristics of the data and developing necessary tools for processing it. When it comes to analyzing stereoscopic 3-D images, however, conventional methods for 2-D images need to be modified because stereoscopic 3-D images are created by a cognitive process of the human brain. We present the frequency domain analysis of a stereoscopic 3-D image with a geometry model of human binocular vision, and based on the analysis, we propose a disparity-adaptive anti-aliasing filter. From our analysis, we concluded that there is an inherent cause of aliasing due to frequency scaling by disparity. The analysis result is consistent with the knowledge obtained from past experiences, which supports the reliability of the analysis, and the proposed filter can be used for reducing undesirable visual discomfort.