Optimization of fast block motion estimation algorithms

There are basically three approaches for carrying out fast block motion estimation: (1) fast search by a reduction of motion vector candidates; (2) fast block-matching distortion (BMD) computation; and (3) motion field subsampling. The first approach has been studied more extensively since different ways of reducing motion vector candidates may result in significantly different performance; while the second and third approaches can in general be integrated into the first one so as to further accelerate the estimation process. In this paper, we first formulate the design of good fast estimation algorithms based on motion vector candidate reduction into an optimization problem that involves the checking point pattern (CPP) design via minimizing the distance from the true motion vector to the closest checking point (DCCP). Then, we demonstrate through extensive studies on the statistical behavior of real-world motion vectors that the DCCP minimization can result in fast search algorithms that are very efficient as well as highly robust. To further utilize the spatiotemporal correlation of motion vectors, we develop an adaptive search scheme and a hybrid search idea that involves a fixed CPP and a variable CPP. Simulations are performed to confirm their advantages over conventional fast search algorithms