Compressing sets of similar images using hybrid compression model

A new compression scheme called the hybrid compression model (HCM) is proposed for compressing sets of similar images. The HCM employs the region growing technique to partition the median image of a set of similar images; and furthermore, it uses the centroid method to characterize the original image data. The differences between the predicted and the original image data are stored and encoded for later use. The efficacy of its application on progressive transmission of similar images over the networks is also studied. The experimental results on various images show that our method provides significant improvement in compression efficiency, ranging from 5.6% to 134.9% in comparison with traditional centroid methods.