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.

本研究基于生成对抗网络构建了一个图像压缩系统，其中包括编码器、解码器/生成器和多尺度鉴别器，并使用全生成式学习压缩目标。模型可合成存储受限的细节，实现在比之前方法失败且出现严重伪影的比特率下，较视觉上令人满意的结果。此外，如果有原始图像的语义标签映射可用，则本方法可以从标签映射中合成出解码后图像的不重要区域，例如街道和树，并相应地减少存储成本。一个用户研究证实，即使使用两倍以上的比特，低比特率下我们的方法都优于现有技术。

极限学习图像压缩的生成对抗网络