Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China.
IEEE Trans Image Process. 2010 May;19(5):1248-58. doi: 10.1109/TIP.2009.2039055. Epub 2009 Dec 22.
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.
本文提出了一种运动对齐自回归（MAAR）模型，用于帧率上转换，其中每个像素通过一个前向 MAAR（Fw-MAAR）模型和一个后向 MAAR（Bw-MAAR）模型的结果平均来进行插值。在 Fw-MAAR 模型中，待插值帧中的每个像素通过在前一帧中的运动对齐的正方形邻域内的像素进行线性加权求和来生成。为了得出更准确的插值权重，通过相同的权重，还将在下一帧中估计对齐的实际像素作为通过相同权重对要插值的帧中的新插值像素的线性加权和。因此，可以通过前一帧中的扩展正方形邻域内的对应像素的加权和来估计下一帧中的后向对齐的实际像素。Bw-MAAR 以相反的方向执行相同的操作。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。广泛的实验表明，所提出的 MAAR 模型能够比传统的帧插值方法（如 MCI、OBMC 和 AOBMC）获得更好的性能，并且对于大多数具有中等或大运动的测试序列，它甚至比 STAR 模型更好。
