A block-based MAP segmentation for image compressions

In this paper, a novel block-based image segmentation algorithm using the maximum a posteriori (MAP) criterion is proposed. The conditional probability in the MAP criterion, which is formulated by the Bayesian framework, is in charge of classifying image blocks into edge, monotone, and textured blocks. On the other hand, the a priori probability is responsible for edge connectivity and homogeneous region continuity. After a few iterations to achieve a deterministic MAP optimization, we can obtain a block-based segmented image in terms of edge, monotone, or textured blocks. Then, using a connected block-labeling algorithm, we can assign a number to all connected homogeneous blocks to define an interior of a region. Finally, uncertainty blocks, which are not given any region number yet, are assigned to one of neighboring homogeneous regions by a block-based region-growing method. During this process, we can also check the balance between the accuracy and the cost of the contour coding by adjusting the size of the uncertainty blocks. Experimental results show that the proposed algorithm yields larger homogeneous regions which are suitable for the object-based image compression.