Granular computing is closely related to the depth of the detail of information with which we are presented, or choose to process. In spatial cognition and image processing such detail is given by the resolution of a picture. The quadtree representation of an image offers a quick look at the image at various stages of granularity, and successive quadtree representations can be used to represent change. For a given image, the choice of quadtree root node plays an important role in its quadtree representation and final data compression. The goal of this paper is to present a heuristic algorithm for finding a root node of a region quadtree, which is able to reduce the number of leaf nodes when compared with the standard quadtree decomposition. The empirical results indicate that the proposed algorithm improves the quadtree representation and data compression when compared to the traditional method.
['Xiang Yin', 'Ivo Düntsch', 'Günther Gediga']
Quadtree representation and compression of spatial data