Adversarial Video Compression Guided by Soft Edge Detection

Sungsoo Kim, Jin Soo Park, Christos G. Bampis, Jaeseong Lee, Mia K. Markey, Alexandros G. Dimakis, Alan C. Bovik

Abstract: We propose a video compression framework using conditional Generative Adversarial Networks (GANs). We rely on two encoders: one that deploys a standard video codec and another one which generates low-level soft edge maps. For decoding, we use a standard video decoder as well as a decoder that is trained using a conditional GAN. Recent "deep" approaches to video compression require multiple videos to pre-train generative networks that conduct interpolation. By contrast, our scheme trains a generative decoder that requires only a small number of key frames and edge maps taken from a single video, without any interpolation. Experiments on two video datasets demonstrate that the proposed GAN-based compression engine is a promising alternative to traditional video codec approaches that can achieve higher quality reconstructions for very low bitrates.