Faster RIFE:为视频框架内插优化实时中期流动估算 (FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation)

The problem of video inter-frame interpolation is an essential task in the field of image processing. Correctly increasing the number of frames in the recording while maintaining smooth movement allows to improve the quality of played video sequence, enables more effective compression and creating a slow-motion recording. This paper proposes the FastRIFE algorithm, which is some speed improvement of the RIFE (Real-Time Intermediate Flow Estimation) model. The novel method was examined and compared with other recently published algorithms. All source codes are available at https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos

翻译：图像处理领域的一个重要任务就是视频间跨框架的内插问题。正确增加录制框数,同时保持平稳移动,可以提高播放视频序列的质量,能够更有效地压缩和创建慢动记录。本文建议采用快速的FastRIFE算法,这是RIFE(实时中间流动估计)模型的某种速度改进。对新颖方法进行了审查,并与最近公布的其他算法进行了比较。所有源代码都可在https://gitlab.com/malwinq/interpologation-of-images-f-slow-movement-views查阅。所有源代码都可以在https://gitlab.com/malwinq/interpologation-images-f-images-f-slow-movey-views查阅。