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Frame rate upconversion using pyramid structure and dense motion vector fields

机译:使用金字塔结构和密集运动矢量场的帧速率上转换

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We propose a frame rate upconversion (FRUC) method using pyramid structures (PS) and dense motion vector fields (MVFs). In FRUC processes, performance is dominantly dependent on motion compensation, thus motion vectors (MVs) must be precisely estimated. Variable sizes of blocks and large search ranges are needed to estimate the MVs of large objects and large movements; however, we use PS and dense MVFs to estimate MVs for various conditions. In the PS, we first estimate MVs on level 0, which is the most reduced image in the PS (L-1 times downsampling), and MVs on the high levels are estimated except for pixels having large corresponding MVs on the lower levels. Integration of MVFs for all levels is followed by a vector median filter to remove noises. Finally, a motion compensated frame is interpolated by weight-overlapped block motion compensation. (C) 2016 SPIE and IS&T
机译:我们提出了一种使用金字塔结构(PS)和密集运动矢量场(MVF)的帧速率上转换(FRUC)方法。在FRUC过程中,性能主要取决于运动补偿,因此必须精确估计运动矢量(MV)。需要可变大小的块和较大的搜索范围来估计大型物体和大型运动的MV。但是,我们使用PS和密集MVF来估算各种条件下的MV。在PS中,我们首先估计级别0的MV,这是PS中缩小最多的图像(L-1倍下采样),并且估计高级别的MV,除了在较低级别具有较大对应MV的像素。对所有级别的MVF进行积分后,再进行矢量中值滤波以去除噪声。最后,通过权重重叠的块运动补偿对运动补偿的帧进行插值。 (C)2016 SPIE和IS&T

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