High-Fidelity Generative Image Compression

Fabian Mentzer

ETH Zurich*

George Toderici

Google Research

Michael Tschannen

Google Research

Eirikur Agustsson

Google Research

*Work done while interning at Google.


We combine Generative Adversarial Networks with learned compression to obtain a state-of-the-art generative lossy compression system. In the paper, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In a user study, we show that our method is preferred to previous state-of-the-art approaches even if they use more than 2× the bitrate.


Interactive Demo comparing our method (HiFiC) to JPG or BPG:

Drag Slider to Compare


PDF including supplementary material available on arXiv

Visual Supplementary

The PDF on arxiv includes the supplementary materials with more information.

Additional visual results are hosted as a PDF here.

Evaluation Images

We evaluate our method on CLIC2020, DIV2K, and Kodak.

Reconstructions of HiFiC on all these datasets can be found here.

User Study

The following shows normalized scores for the user study, compared to perceptual metrics, where lower is better for all.

HiFiC is our method. M&S is the deep-learning based Mean & Scale Hyperprior, from Minnen et al., optimized for mean squared error. BPG is a non-learned codec based on H.265 that achieves very high PSNR. No GAN is our baseline, using the same architecture and distortion as HiFiC, but no GAN. Below each method, we show average bits per pixel (bpp) on the images from the user study, and for learned methods we show the loss components.

The study shows that training with a GAN yields reconstructions that outperform BPG at practical bitrates, for high-resolution images. Our model at 0.237bpp is preferred to BPG even if BPG uses 2.1× the bitrate, and to MSE optimized models even if they use 1.7× the bitrate.


Trained model and TensorFlow code coming soon!


  title={High-Fidelity Generative Image Compression},
  author={Mentzer, Fabian and Toderici, George and Tschannen, Michael and Agustsson, Eirikur},
  journal={arXiv preprint arXiv:2006.09965},