Sparse prediction for compression of stereo color images conditional on constant disparity patches

This paper introduces an algorithm for lossless encoding of color stereo images using sparse prediction and context coding. For encoding the left color image, an extension of the method uses additionally conditioning on the warped image, obtained by warping the right color image using the information in the disparity image. Different sparse predictors are designed and used at the locations of large constant patches of the disparity image. The new method of stereo color image compression is shown to perform better than several publicly available lossless image compressors over the Middlebury dataset. Besides compression applications, the method can be used for computing an implementable minimum description length cost for ranking candidate disparity map images, according to their performance in the warping process.