Generative Adversarial Networks
for Extreme Learned Image Compression

Click here to compare with: BPG, WebP, JPEG2000, JPEG, Original

Paper

Eirikur Agustsson*, Michael Tschannen*, Fabian Mentzer*, Radu Timofte and Luc Van Gool
Generative Adversarial Networks for Extreme Learned Image Compression,
ICCV 2019.
[BibTeX] [PDF on arXiv] 
@inproceedings{agustsson2019generative,
  title={Generative adversarial networks for extreme learned image compression},
  author={Agustsson, Eirikur and Tschannen, Michael and Mentzer, Fabian and Timofte, Radu and Gool, Luc Van},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={221--231},
  year={2019}
}

Team

Results

User Study

The following figure shows the results of a userstudy. We can see that our method is prefered over BPG, even when BPG uses almost twice the number of bits.

The bitrate of our models is highlighted on the x-axis with a black diamond. The thick gray line shows the percentage of users preferring our model to BPG at that bitrate (bpp). The blue arrow points from our model to the highest-bitrate BPG operating point where more than 50% of users prefer ours, visualizing how many more bits BPG uses at that point. In the paper, we obtain similar results for the RAISE1k dataset and Cityscapes.

Semantic Preservation

We measure the preservation of semantics through the mIoU of a pre-trained PSPNet for semantic segmentation on CityScapes in the following Figure.

We obtain a significantly higher mIoU compared to BPG, which is further improved when guiding the training with semantics.

Selective Synthesis

Our method allows for selectively preserving some regions while fully synthesizing the rest of the image (keeping the semantics in tact).

Check the the paper for more details!