Segmentation based reversible image compression
Reversible compression of images has been the topic of considerable research as it finds applications in many fields in which the deviation of the reproduced image from the original image is intolerable, however small be the deviation. This paper is concerned with the problem of reducing spatial redundancies in gray scale images, thus providing effective lossless compression, using segmentation information. We will present new edge models that deal effectively with two issues that make such models normally unsuitable for compression applications: local applicability and large number of parameters needed for representation. Segmentation information is provided by a recent transform (1993), which we found to possess qualities making it especially suitable for compression. The final residual image is obtained using autocorrelation-based 2-D linear prediction. Different implementations providing lossless compression are presented along with results over a number of common test images. Results show that the proposed approach can be used to yield robust lossless compression, while providing consistently and significantly better results than the best possible JPEG lossless coder.