Enhanced Correlation Matching based Video Frame Interpolation
Sungho Lee, Narae Choi, Woong Il Choi

TL;DR
This paper introduces a high-resolution video frame interpolation method using enhanced correlation matching and recurrent pyramid architecture, improving accuracy in challenging scenarios like large motion and occlusion.
Contribution
It presents a novel DNN framework with recursive flow refinement and correlation-based matching, optimized for 4K resolution and large motions, outperforming previous methods.
Findings
Outperforms previous methods on 4K video data
Achieves higher quality in low-resolution benchmarks
Uses fewer model parameters for comparable or better results
Abstract
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…
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Videos
Enhanced Correlation Matching based Video Frame Interpolation· youtube
Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Image Processing Techniques · Video Coding and Compression Technologies
