Exploiting local auto-correlation function for fast video to reference image alignment

Digital images of natural scenes are usually characterized by strong spatial correlation between adjacent pixels which has been successfully exploited in the coding of still and moving pictures. In this work we show that the strong spatial correlation of natural images can also be used to speedup the video to reference image alignment algorithms. To this end, we divide the search locations in the reference image into groups. The target frames are matched with only one location in each group, while on the remaining locations we evaluate exact theoretic upper bounds on the correlation coefficient. These bounds are used to eliminate majority of the search locations and thus result in significant speedup without effecting the value or location of the global maxima. In our experiments, up to 83.3% search locations are found to be eliminated and the speedup is up to 5.3 times the FFT based implementation and up to 7.9 times the spatial domain techniques.