Efficient compression of disparity data is important for accurate view synthesis purposes in multi-view communication systems based on the “texture plus depth” format, including the stereo case. In this paper a novel technique for lossless compression of stereo disparity images is presented. The coding algorithm is based on bit-plane coding, disparity prediction via disparity warping and context-based arithmetic coding exploiting predicted disparity data. Experimental results show that the proposed compression scheme achieves average compression factors of about 48:1 for high resolution disparity maps for stereo pairs and outperforms different standard solutions for lossless still image compression. Moreover, it provides a progressive representation of disparity data as well as a parallelizable structure.