A feature-assisted search strategy for block motion estimation

Abstract

Block motion estimation using the exhaustive full search is computationally intensive. Previous fast search algorithms tend to reduce the computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on the assumption: the MAD distortion function increases monotonically as the search location moves away from the global minimum. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighbourhood around the global minimum. Consequently, one simple, but perhaps the most efficient and reliable strategy, is to put the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of some feature points. After a feature detecting process was applied to each frame to extract a set of feature points as matching primitives, we studied extensively the statistical behaviour of these matching primitives and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient.