Generative Adversarial Networks for Extreme Learned Image Compression

Résumé : 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.