Low Complexity Motion Estimation Based on Spatio-temporal Correlations

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. For this reason, low complexity motion estimation algorithms are viable solutions. In this paper, we present an efficient algorithm based on exploiting temporally and spatially correlated motion information that defines the search pattern and the location of search starting point adaptively. Experiments show that the speedup improvement of the proposed algorithm over Diamond Search algorithm (DS) and HEXagon-Based Serch (HEXBS) can be up to 2 ~ 3 times faster and the image quality improvement can be better up to 0.1 ~ 1(dB).