Image coding based on segmentation using region growing

This paper describes a composite source model for images. An image is segmented into uniform and homogeneous regions using centroid linkage region growing algorithms. The region homogenity is determined by the Student T-statistics. Excessive regions resulting from region growing are merged according to region merging rules. The initially segmented image is then clustered into classes with the help of the K-means algorithm. The image classes are shown in this paper as visual aids in judging the classification procedure. The class numbers and the corresponding pixel counts are also included. Finally, as an application of composite source models, an image is encoded using matrix quantizers with separate codebook for each class of the image.