FrameRS: A Video Frame Compression Model Composed by Self supervised
  Video Frame Reconstructor and Key Frame Selector
Qiqian Fu Guanhong Wang Gaoang Wang
In this paper, we present frame reconstruction model: FrameRS. It consists
self-supervised video frame reconstructor and key frame selector. The frame
reconstructor, FrameMAE, is developed by adapting the principles of the Masked
Autoencoder for Images (MAE) for video context. The key frame selector, Frame
Selector, is built on CNN architecture. By taking the high-level semantic
information from the encoder of FrameMAE as its input, it can predicted the key
frames with low computation costs. Integrated with our bespoke Frame Selector,
FrameMAE can effectively compress a video clip by retaining approximately 30%
of its pivotal frames. Performance-wise, our model showcases computational
efficiency and competitive accuracy, marking a notable improvement over
traditional Key Frame Extract algorithms. The implementation is available on
Github