Object-Based Motion Estimation Using the EPD Similarity Measure

Abstract

Effective motion compensated prediction plays a significant role in efficient video compression. Image registration can be used to estimate the motion of the scene in a frame by finding the geometric transformation which automatically aligns reference and target images. In the video coding literature, image registration has been applied to find the global motion in a video frame. However, if the motion of individual objects in a frame is inconsistent across time, the global motion may provide a very inefficient representation of the true motion present in the scene. In this paper we propose a motion estimation algorithm for video coding using a new similarity measure called the edge position difference (EPD). This technique estimates the motion of the individual objects based on matching the edges of objects rather than estimating the motion using the pixel values in the frame. Experimental results demonstrate that the proposed edge-based similarity measure approach achieves superior motion compensated prediction for objects in a scene when compared to the approach which only considers the pixel values of the frame.