EPD Similarity Measure and Demons Algorithm for Object-Based Motion Estimation

Reduction of the temporal redundancies among frames, which can be achieved by the proper motion-compensated prediction, is the key to efficient video compression. Image registration is a technique, which can be exploited to find the motion between the frames. As the motion of an individual scene in a frame is varying across time, it is important to find the motion of the individual object for efficient motion-compensated prediction instead of finding the global motion in a video frame as has been used in the video coding literature. In this paper, we propose a motion estimation technique for video coding that estimates the correct motion of the individual object rather than estimating the motion of the combination of objects in the frame. This method adopts a registration technique using a new edge position difference (EPD) similarity measure to separate the region of individual objects in the frame. Then we apply either EPD-based registration or the Demons registration algorithm to estimate the true motion of each object in the frame. Experimental results show that the proposed EPD-Demons registration algorithm achieves superior motion-compensated prediction of a frame when compared to the global motion estimation-based approach.