Session 11D5 Data Compression Techniques For Maps

The efficiencies of various data compression techniques as applied to color maps are compared. These color maps have certain special characteristics such as large homogeneous regions and fine detail such as lines and lettering. The color maps are first classified using the K means clustering algorithm with neighborhood classification. Three techniques are investigated- contour, quadtree and rmn-length coding. The run length coding algorithm is modified to allow wrap around of runs. A modification of the standard binary image quadtree compression algorithm for color images is introduced. In quadtree coding a modified eldest-son eldest younger sibling quadtree is used to reduce memory requirement in storing the quadtree. Lempel-Ziv compression is applied to the classified and unclassified images as also to the output of the compression algorithms. The algorithms will be compared on the compression ratios achieved