Region-based near-lossless image compression

We present a near-lossless technique for the compression of images, which is based on the partitioning of the image into regions of constant intensity. The boundary information associated with the image partition is encoded with the method of the transition points. The compression of the intensities of the regions is based on the usual entropy encoding of the context-modeled prediction residuals. The experimental results show that this approach is able to provide significant compression improvements in images having sparse histograms, for small L/sub /spl infin// errors.