Segmentation based coding of motion compensated prediction error images

A novel segmentation-based method for coding motion-compensated prediction error images (PEIs) is described. The PEIs result from various motion-compensated techniques, e.g., block matching and pel-recursive techniques. A detailed study on the statistics of these kinds of images is carried out and shows that the correlation in the PEIs is very low compared with that in typical natural images. Therefore, the conventional transform coding or subband coding is not appropriate for the PEIs. The proposed method segments a PEI using dynamic thresholding and morphological operations. Various morphological operators are applied, resulting in a final clean image and a relatively small number of segments. The contour and the interior region are coded separately using entropy coding techniques. Comparisons with the DCT (discrete cosine transform) show that the proposed algorithm outperforms the DCT-based algorithm in terms of both PSNR and subjective visual quality.<>