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In the context of motion estimation for video coding, successive elimination algorithms (SEAs) significantly reduce the number of candidates evaluated during motion estimation without altering the resulting optimal motion vector. Nevertheless, SEA is often only used in conjunction with exhaustive search algorithms (e.g., full search). In this paper, we combine the multi-level successive elimination algorithm (ML-SEA) and the rate-constrained successive elimination algorithm (RCSEA) and show that they can be advantageously applied to suboptimal search algorithms. We demonstrate that the savings brought about by the new multi-level RCSEA (ML-RCSEA) outweigh the pre-computational costs of this approach for the Test Zonal (TZ) Search algorithm found in the HM reference encoder. We propose a novel multi-level composition pattern for performing RCSEA on an asymmetric partitioning. We introduce a double-check mechanism for RCSEA, and show that on average, it avoids computing 71% of motion vector (MV) costs. We also apply the proposed ML-RCSEA to bi-predictive refinement search and leverage a cost-based search ordering to remove 56% of error metric computations, on average. When compared to the HM reference encoder, our experiments show that the proposed solution reduces the TZ Search time by approximately 45%, contributing to an average encoding time reduction of about 7%, without increasing the Bjontegaard delta rate (BD-Rate).

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.

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.