As the scope of virtual reality applications including stereoscopic imaging becomes wider, it is quite clear that not every designer of a VR application thinks of its constraints in order to make a correct use of stereo. Stereoscopic imagery though not required can be a useful tool for depth perception. It is possible to limit the depth of field as shown by Perrin who has also undertaken research on the link between the ability of fusing stereoscopic images (stereopsis) and local disparity and spatial frequency content. We will show how we can extend and enhance this work especially on the computational complexity point of view. The wavelet theory allows us to define a local spatial frequency and then a local measure of stereoscopic comfort. This measure is based on local spatial frequency and disparity as well as on the observations made by Woepking. Local comfort estimation allows us to propose several filtering methods to enhance this comfort. The idea to modify the images such as they check a “stereoscopic comfort condition” defined as a threshold for the stereoscopic comfort condition. More technically, we seek to limit high spatial frequency content when disparity is high thanks to the use of fast algorithms.