Lossless compression of high resolution disparity map images

High resolution disparity images are stored in floating point raw files, where the number of bits per pixel is typically 32, although the number of used bits when converted to a fixed point representation is lower, e.g., between 24 and 26 in the dataset used in our experiments. In order to compress images with such high dynamic range, the bitplanes of the original image are combined into integer images with at most 16 bits, for which readily existing compressors are available. We introduce first a context predictive compressor (CPC) which can operate on integer images having more than 16 bits. The proposed overall compression scheme uses a first revertible linear transformation of the image as a first decorrelation process, and then splits the transformed image into integer images with smaller dynamic range, which are finally encoded. We experiment with schemes of split-into-2 and split-into-3, with combinations of several existing compressors for the integer image components and show that the newly introduced CPC operating over the least significant bitplanes combined with CERV operating over the most significant bitplanes achieves always the best compression, with final lossless compressed results of between 8 and 12 bits per pixel.