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We propose a GAN-based image compression method working at extremely lowbitrates below 0.1bpp. Most existing learned image compression methods sufferfrom blur at extremely low bitrates. Although GAN can help to reconstruct sharpimages, there are two drawbacks. First, GAN makes training unstable. Second,the reconstructions often contain unpleasing noise or artifacts. To addressboth of the drawbacks, our method adopts two-stage training and networkinterpolation. The two-stage training is effective to stabilize the training.Moreover, the network interpolation utilizes the models in both stages andreduces undesirable noise and artifacts, while maintaining important edges.Hence, we can control the trade-off between perceptual quality and fidelitywithout re-training models. The experimental results show that our model canreconstruct high quality images. Furthermore, our user study confirms that ourreconstructions are preferable to state-of-the-art GAN-based image compressionmodel. The code will be available.

Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks