Scalable and robust image compression using quadtrees

We introduce a new scalable image encoding technique which is geared to support image communication over unreliable channels. Our image encoding technique is based on the quadtree representation of images and vector quantization. We use a mean-removal technique to separate the means image and the difference image. The difference image is then encoded as a breadth-first traversal of the quadtree corresponding to the image. Vector quantization is then used to compress the quadtree nodes based on the spatial locality of the quadtree data. The number of bits allocated to each node varies in each level according to its priority. We show that this encoding is time-efficient to generate. In addition, we demonstrate its ability to tolerate and conceal error. Moreover, we illustrate the ability of our encoding technique to react to changing network traffic patterns. The new encoding technique proved to perform well in all aspects. We plan to use this image encoding technique as the basis for deriving a new family of scalable and robust video encoding techniques.