In this paper, a bi-directional stereo matching algorithm based-on adaptive matching window is proposed. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. Especially, in the proposed algorithm, first feature values are extracted from input stereo images pair. Then, a matching window for stereo matching is adaptively selected depending on the magnitude of these feature values. That is, for the region having larger feature values, a smaller matching window is selected while, for the opposite case, a larger matching window is selected by comparing predetermined threshold values. This approach is not only able to reduce a mismatching of disparity vectors which occurs in the conventional dense disparity estimation with a small matching window, but is also able to reduce blocking effects which occur in the coarse disparity estimation with a large matching window. In addition, from some experiments using stereo sequences of 'Man' and 'Fichier', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 6.78 dB on average at ± 30 search ranges by comparing with that of conventional algorithms. And also, it is found that there is almost no difference between an original image and a reconstructed image through the proposed algorithm by comparison to that of conventional algorithms.