Generative Adversarial Networks for Extreme Learned Image Compression

Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc van Gool

Abstract: 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, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.

Keywords: Learned compression, generative adversarial networks, extreme compression

TL;DR: GAN-based extreme image compression method using less than half the bits of the SOTA engineered codec while preserving visual quality

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1804.02958/code)