Abstract
We present an adaptive algorithm that finds the best block-matching results in a computationally constrained and varied environment. The conventional diamond search algorithm, though faster than most known algorithms, is not very robust for sequences with scene variations or significant global motion. To solve this issue, rather than only using one fast motion estimation algorithm, we devise a more adaptive selection of fast motion estimation algorithms. Our adaptive selection approach for fast block search (ASFBS) algorithm uses a diamond search and two new subalgorithms: a cross-three-step search algorithm for large moving images and an advanced cross-diamond search algorithm for small moving images. The proposed ASFBS adapts based on the length of the motion vector, the number of search points, and the matching criteria of the neighboring block. Experimental results show that ASFBS is much more robust; it is faster than other popular fast block-matching algorithms, with smaller distortions.
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