A Fast Block Matching Algorithm Based on the Winner-Update Strategy

Block matching is a popular and powerful technique for stereo vision, visual tracking, object recognition, and video compression. This paper presents a new fast algorithm, which is called the winner-update algorithm, for block matching. We utilize an ascending list of the lower bounds of the matching error for each search position. The calculation of the matching error can be avoided if one of its lower bound is larger than the globally minimum matching error. The winner-update algorithm computes the lower bounds only when the previous lower bounds in the same list are smaller than the globally minimum matching error. This algorithm can significantly speed up the computation of the block matching because (1) the computational cost of each lower bound is less than that of the matching error; and (2) for many search positions, only the first several lower bounds in the list need to be calculated. In the application to motion vector estimation, our experiments on test image sequences show that up to 98 % of operations can be reduced while still guaranteeing the minimum matching error