Video frame interpolation is a technology that generates high frame rate videos from low frame rate videos by using the correlation between consecutive frames. Presently, convolutional neural networks (CNN) exhibit outstanding performance in image processing and computer vision. Many variant methods of CNN have been proposed for video frame interpolation by estimating either dense motion flows or kernels for moving objects. However, most methods focus on estimating accurate motion. In this study, we exhaustively analyze the advantages of both motion estimation schemes and propose a cascaded system to maximize the advantages of both the schemes. The proposed cascaded network consists of three autoencoder networks, that process the initial frame interpolation and its refinement. The quantitative and qualitative evaluations demonstrate that the proposed cascaded structure exhibits a promising performance compared to currently existing state-of-the-art-methods.

视频帧插值是一种通过使用连续帧之间的相关性从低帧频视频生成高帧频视频的技术。目前，卷积神经网络（CNN）在图像处理和计算机视觉中表现出出色的性能。通过估计密集运动流或运动对象的内核，已经提出了许多CNN变体方法用于视频帧插值。但是，大多数方法着重于估计准确的运动。在这项研究中，我们详尽地分析了两种运动估计方案的优势，并提出了一种级联系统以最大化两种方案的优势。所提出的级联网络由三个自动编码器网络组成，用于处理初始帧内插及其改进。