We propose a novel DNN based framework called the Enhanced Correlation
Matching based Video Frame Interpolation Network to support high resolution
like 4K, which has a large scale of motion and occlusion. Considering the
extensibility of the network model according to resolution, the proposed scheme
employs the recurrent pyramid architecture that shares the parameters among
each pyramid layer for optical flow estimation. In the proposed flow
estimation, the optical flows are recursively refined by tracing the location
with maximum correlation. The forward warping based correlation matching
enables to improve the accuracy of flow update by excluding incorrectly warped
features around the occlusion area. Based on the final bi-directional flows,
the intermediate frame at arbitrary temporal position is synthesized using the
warping and blending network and it is further improved by refinement network.
Experiment results demonstrate that the proposed scheme outperforms the
previous works at 4K video data and low-resolution benchmark datasets as well
in terms of objective and subjective quality with the smallest number of model
parameters.

该研究提出了一种基于 DNN 的新框架，名为 ECM-VFI，用于高分辨率视频帧插值，包括具有大规模运动和遮挡的 4K 视频数据。通过递归追踪最大相关位置来改善光流更新的准确性。采用前向曲面匹配可以通过排除遮挡区域周围的错误曲面特征来提高更新精度和提高细化和混合网络生成的任意时间点的中间帧的质量。实验结果显示，该方案在 4K 视频数据和低分辨率基准数据集方面比以前的作品表现更优，并且具有最少的模型参数。