Adversarial Video Compression Guided By Soft Edge Detection
Sungsoo Kim, Jin Soo Park, Christos Bampis, Jaeseong Lee, Mia Markey, Alexandros Dimakis, Alan Bovik
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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.