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MAPS: a new and efficient block-matching criterion for motion estimation

机译:MAPS:一种新的高效的运动估计块匹配准则

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In this paper, a novel and efficient block-matching criterion called mean absolute error of projective sum (MAPS) is proposed to significantly reduce the computational complexity of block-based motion estimation. It can save about 78% computational load in comparison with that of the conventional mean absolute difference (MAD). Simulation results show that its prediction quality is close to the MAD's. By applying the partitioning technique in MAPS, the prediction quality can be further improved such that the proposed criterion can be applied in different applications for motion estimation. Further, according to its simple and regular properties, it is very suitable for VLSI implementation.
机译:本文提出了一种新颖有效的块匹配准则,称为射影和平均绝对误差(MAPS),以显着降低基于块的运动估计的计算复杂度。与传统的平均绝对差(MAD)相比,它可以节省大约78%的计算量。仿真结果表明,该算法的预测质量接近MAD。通过在MAPS中应用分区技术,可以进一步提高预测质量,从而可以将所提出的标准应用于运动估计的不同应用中。此外,根据其简单和常规的特性,它非常适合VLSI实现。

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