Image Compression Based on Centipede Model

We present an efficient contour based image coding scheme based on Centipede Model. 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 for each edge segment which lies along the segment and the gray level variation across an edge point is represented by the difference between footholds and distance between left and right feet of the centipede. We obtain edges by using the recently introduced Generalized Edge Detector (GED) [1] which controls the scale and shape of the filter, providing edges suitable to the application in hand. The detected edge segments are ranked based on the weighted sum of the length of the segment, mean contrast and standard deviation of gray values on the segment. 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 image is reconstructed from this sparse information by minimizing a hybrid energy functional which spans a space called Λτ-space. Since the GED filters are derived from this energy functional, we utilized the same process for detecting the edges and reconstructing the surface from them. The proposed model and the algorithm have been tested on both real and synthetic images. Compression ratio reaches to 180:1 for synthetic images while it ranges from 25:1 to 100:1 for real images. We have experimentally shown that the proposed model preserves perceptually important features even at the high compression ratios.