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Efficient Hierarchical Motion Estimation Algorithm and Its VLSI Architecture

机译:高效的分层运动估计算法及其VLSI架构

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This paper addresses the development and hardware implementation of an efficient hierarchical motion estimation algorithm, HMEA, using multiresolution frames to reduce the computational complexity. Excellent estimation performance is ensured using an averaging filter to downsample the original image. At the smallest resolution, the least two motion vector candidates are selected using a full-search block matching algorithm. At the middle level, these two candidate motion vectors are employed as the center points for small range local searches. Then, at the original resolution, the final motion vector is obtained by performing a local search around the single candidate from the middle level. HMEA exhibits regular data flow and is suitable for hardware implementation. An efficient VLSI architecture that includes an averaging filter to downsample the image and two 2-D semisystolic processing element arrays to determine the sum of absolute difference in pipeline is also presented. Simulation results indicate that HMEA is more area-efficient and faster than many full-search and multiresolution architectures while maintaining high video quality. This architecture with 59K gates and 1393 bytes of RAM is implemented for a search range of [ -16.0, +15.5].
机译:本文介绍了一种有效的分层运动估计算法HMEA的开发和硬件实现,该算法使用多分辨率帧来降低计算复杂度。使用平均滤波器对原始图像进行下采样可以确保出色的估计性能。在最小分辨率下,使用全搜索块匹配算法选择至少两个运动矢量候选。在中间层,这两个候选运动矢量被用作小范围本地搜索的中心点。然后,以原始分辨率,通过围绕来自中间层的单个候选者执行局部搜索来获得最终运动矢量。 HMEA具有规则的数据流,适合硬件实施。还提出了一种有效的VLSI体系结构,该体系结构包括对图像进行降采样的平均滤波器和两个2-D半收缩处理元素阵列,以确定流水线中绝对差之和。仿真结果表明,HMEA在保持高视频质量的同时,比许多全搜索和多分辨率体系结构在区域效率和速度方面更高。这种具有59K门和1393字节RAM的架构是针对[-16.0,+15.5]的搜索范围实现的。

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