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Recently, many algorithms have been proposed for fast full search motion estimation. Among them, successive elimination algorithm (SEA) and its modified algorithms significantly speed up the performance of the full search algorithm. By introducing the inequality equation between the norm and the mean absolute difference (MAD) of two matching blocks, the SEA can successively eliminate invalid candidate blocks without any loss in estimation accuracy. In this paper, we propose a partial norm based early rejection algorithm (PNERA) for fast block motion estimation. The proposed algorithm employs the sum of partial norms from several subblocks of the block. Applying the sum of partial norms to the inequality equation, we can significantly reduce the computational complexity of the full search algorithm. In an attempt to reduce the computational load further, the modified algorithms using partial norm distortion elimination (PNDE) and subsampling methods are also proposed. Experimental results show that the proposed algorithm is about 4 to 9 times faster than the original exhaustive full search, and is about 3 to 4 times faster than the SEA.

A transform-domain successive elimination algorithm for block-based motion estimation (ME) is proposed. First, a method is introduced for calculating the sum of squared differences in the transform domain. Then, based on this method, a new transform-domain criterion is proposed to eliminate impossible candidates as early as possible. Experimental results show that the proposed algorithm speeds up the ME process by up to a factor of 10 compared with the conventional full search algorithm.

An efficient algorithm is proposed to reduce the computation cost of block matching algorithms for motion estimation in video coding. Based on a new insight in block matching algorithms, we extend the successive elimination algorithm to a multilevel case. By using the sum norms of the blocks and the subblocks, tighter and tighter decision boundaries can be obtained for eliminating the search positions. The efficiency of the proposed algorithm combined with the full search algorithm and several fast search algorithms is verified by simulation results.

This paper presents a simple and effective method to further reduce the search points in multilevel successive elimination algorithm (MSEA). Because the calculated sea values of those best matching search points are much smaller than the current minimum SAD, we can simply increase the calculated sea values to increase the elimination ratio without much affecting the coding quality. Compared with the original MSEA algorithm, the proposed strict MSEA algorithm (SMSEA) can provide average 6.52 times speedup. Compared with other lossy fast ME algorithms such as TSS and DS, the proposed SMSEA can maintain more stable image quality. In practice, the proposed technique can also be used in the fine granularity SEA (FGSEA) algorithm and the calculation process is almost the same.

In this paper, an algorithm, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE is featured by providing a sequence of fine grained boundary levels in an aim to reject a checking candidate as early as possible, thus reducing computational load as much as possible. It is shown that the multilevel successive elimination algorithm (MSEA) is just a degraded case of FGSE with much coarser boundary levels. Comparative experiments are presented to verify the substantial speed-up gain of the proposed algorithm over the MSEA.

Presents a very fast exhaustive search algorithm for motion estimation. This algorithm is based on successive elimination algorithm (SEA) and fast algorithms for the estimation of motion vectors [2]. A new motion estimation method called new fast successive elimination algorithm (NFSEA) finds the same motion vectors as exhaustive search algorithm with far fewer computational load. NFSEA effectively eliminates the search points within the search window and thus decreasing the number of matching evaluations that require very intensive computations. So, NFSEA dramatically reduces the number of operations for finding motion vectors. Experimental results show that NFSEA is a very efficient algorithm for the estimation of motion vectors.

We propose a fast block motion estimation algorithm using the sum of partial norms which finds the same motion vectors as the exhaustive full search algorithm with a reduced computational load. Experimental results show that the proposed algorithm is about 3 or 4 times faster than the original exhaustive full search, and is about 2 times faster than the successive elimination algorithm (SEA).

A successive elimination algorithm for binary block matching motion estimation based on one-bit transform is presented. The principles of the proposed algorithm are mathematically formulated. Simulation result show that the proposed algorithm can significantly reduce the computation time without influencing the prediction performance.