On the study of lossless compression of computer generated compound images

This paper studies the problem of lossless compression of computer generated compound images that contain not only photographic images but also text and graphic images. We present a simple backward adaptive classification scheme to separate the image source into three classes: smooth regions, text regions and image regions. Different probability models are assigned within each class to maximize the compression performance. We also extend our scheme to exploit the interplane dependency for coding color images. The segmentation results of the reference color plane are used as the contexts for the classification and coding of the current color plane. Our new lossless coder significantly outperforms current state-of-the-art coders such as CALX and JPEG-LS for compound images with modest computational complexity.