Motion Estimation Based on Temporal Correlations

Abstract

To remove temporal redundancy contained in a sequence of images, motion estimation techniques have been developed. However, the high computational complexity of the problem makes such techniques very difficult to be applied to high-resolution applications in a real time environment. If a priori knowledge about the motion of the current block is available before the motion estimation, a better starting point for the search of an optimal motion vector can be selected. In this paper, we present a new motion estimation approach based on temporal correlations of consecutive image frames that defines the search pattern and the location of initial search point adaptively. Experiments show that, comparing with DS (Diamond Search) algorithm, the proposed algorithm is about 0.1 ∼ 0.5(dB) better than DS in terms of PSNR and improves as much as 50% in terms of the average number of search points per motion estimation.