A Motion-Aligned Auto-Regressive Model for Frame Rate Up Conversion
A Motion-Aligned Auto-Regressive Model for Frame Rate Up Conversion
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DOI：
10.1109/tip.2009.2039055
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发表时间：
2010-05
影响因子：
10.6
通讯作者：
Yongbing Zhang;Debin Zhao;Siwei Ma;Ronggang Wang;Wen Gao
中科院分区：
文献类型：
--
作者：
Yongbing Zhang;Debin Zhao;Siwei Ma;Ronggang Wang;Wen Gao
本文提出了一种运动对齐自回归（MAAR）模型用于帧速率上转换，其中每个像素被插值为一个前向MAAR（Fw-MAAR）模型和一个后向MAAR（Bw-MAAR）模型产生的结果的平均值。在Fw-MAAR模型中，待插值帧中的每个像素都是作为前一帧中运动对齐的正方形邻域内的像素的线性加权总和而生成的。为了得到更精确的插值权重，还将下一帧中的对齐的实际像素估计为待插值帧中的新插值像素通过相同权重的线性加权总和。因此，可以将下一帧中的向后对准的实际像素估计为前一帧中的放大的正方形邻域内的对应像素的加权和。Bw-MAAR的执行方式与此类似，只是操作方向相反。然后提出了一种阻尼牛顿算法来计算Fw-MAAR和Bw-MAAR模型的自适应插值权重。实验结果表明，MAAR模型的性能优于MCI、OBMC和AOBMC等传统的帧内插方法，对于大多数中等或较大运动的测试序列，MAAR模型的性能甚至优于星星模型。
In this paper, a motion-aligned auto-regressive (MAAR) model is proposed for frame rate up conversion, where each pixel is interpolated as the average of the results generated by one forward MAAR (Fw-MAAR) model and one backward MAAR (Bw-MAAR) model. In the Fw-MAAR model, each pixel in the to-be-interpolated frame is generated as a linear weighted summation of the pixels within a motion-aligned square neighborhood in the previous frame. To derive more accurate interpolation weights, the aligned actual pixels in the following frame are also estimated as a linear weighted summation of the newly interpolated pixels in the to-be-interpolated frame by the same weights. Consequently, the backward-aligned actual pixels in the following frame can be estimated as a weighted summation of the corresponding pixels within an enlarged square neighborhood in the previous frame. The Bw-MAAR is performed likewise except that it is operated in the reverse direction. A damping Newton algorithm is then proposed to compute the adaptive interpolation weights for the Fw-MAAR and Bw-MAAR models. Extensive experiments demonstrate that the proposed MAAR model is able to achieve superior performance than the traditional frame interpolation methods such as MCI, OBMC, and AOBMC, and it is even better than STAR model for the most test sequences with moderate or large motions.