  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.
