An efficient contour based image coding scheme based on Centipede Model [1] is introduced in this paper. Unlike previous contour based models which presents discontinuities with various scales as a step edge of constant scale, the centipede model allows us to utilize the actual scales of discontinuities as well as location and contrast across them. The use of the actual scale of edges together with other properties enables us to reconstruct a better replica of the original image as compared to the algorithm lacking this feature. In this model, there is a centipede structure for each edge segment which lies along the edge segment and the gray level variation across an edge point is represented by a hybrid spline and distance between left and right feet of the centipede. The proposed model aims at reconstructing the closest intensity surface to the original one from the contour information. We first obtain edges by using the Generalized Edge Detector (GED) [2, 3] which controls the scale and shape of the filter, providing edges suitable to the application in hand. Then the detected edges are traced to produce the distinct contours. These contours are ranked by assigning a priority based on the weighted sum of contour length, mean contrast, contour density and mean contour curvature. In our scheme, the compression ratio is controlled by retaining the most significant segments and by adjusting the distance between the successive foot pairs. The original