New results on exhaustive search algorithm for motion estimation using adaptive partial distortion search and successive elimination algorithm

Motion estimation is one of the most computational-intensive tasks in video compression. In order to reduce the amount of computation, various fast motion estimation algorithms have been developed. These fast algorithms can be classified into two groups. One is the lossy motion-estimation approach, which may have some degradation of predicted images, and the other is lossless, which means that the quality of the predicted images is exactly the same as those obtained by the conventional full search algorithm. The partial distortion search and successive elimination algorithm are two well-known techniques belonging to the second kind of approach. These two algorithms use different checking criteria to eliminate as much redundant computations as possible. Actually, the working principles of these methods are independent to each others and it is possible to apply them sequentially in order to achieve greater saving in computation. In this paper, we propose a new fast full-search motion estimation algorithm which can exploit fully the advantages of adaptive partial distortion search and successive elimination algorithm. Experimental results show that this proposed algorithm has an average speed-up of 13.31 as compared with the full search algorithm in terms of computational efficiency. This result is much better than the method simply combing both partial distortion search and successive elimination algorithm, which has an average computational speed-up of 10.60. For a practical realization using a PC, the average execution time speed-up for our algorithm is 4.96, which is also the best performance among all algorithms tested