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18 October 1999 Efficient and adaptive three-step search (EATSS) algorithm using adaptive search strategy, unequal subsampling, and partial distortion elimination
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Abstract
Three-step search (TSS) has been studied for low bit-rate video communications because of significantly reduced computation, simplicity and reasonable performance in the motion estimation. Many other modified TSS algorithms have been developed for higher speedup of computation and improved error performance of motion compensation. Among the modified TSS algorithms, new three-step search (NTSS) shows very good error performance with more reduced computation on the average. However, the method can exceed about 30% compared with the computation of TSS for some cases. It can be serious problem in real-time video coding at the worst case. This paper proposes an efficient and adaptive three-step search (EATSS) algorithm with adaptive search strategy considering unequal subsampling and partial distortion elimination (PDE). Proposing search strategy reduces useless checking points of the first step in the NTSS by using initial sum of absolute difference (SAD) and predefined threshold of SAD. Instead of checking 17 candidate points in the first step as the NTSS, our search algorithm starts with 1 or 9 checking points according to the comparison between initial SAD and predefined threshold of SAD. Experimentally, our algorithm shows good performance in terms of PSNR of predicted images and average checking points for each block compared with NTSS and TSS.
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Jong-Nam Kim and Tae-Sun Choi "Efficient and adaptive three-step search (EATSS) algorithm using adaptive search strategy, unequal subsampling, and partial distortion elimination", Proc. SPIE 3808, Applications of Digital Image Processing XXII, (18 October 1999); https://doi.org/10.1117/12.365866
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