Extreme Learned Image Compression with GANs

Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2587-2590


We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator. Additionally, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from a semantic label map extracted from the original image, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach significantly outperforms state-of-the-art methods, saving up to 67% compared to the next-best method BPG.

Related Material

author = {Agustsson, Eirikur and Tschannen, Michael and Mentzer, Fabian and Timofte, Radu and Van Gool, Luc},
title = {Extreme Learned Image Compression with GANs},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}