We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, 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, proportionally reducing the storage cost. 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.
Agustsson et al. REF propose a multi-scale discriminator in a generative adversarial network (GAN) for low-bitrate compression.
Generative Adversarial Networks for Extreme Learned Image Compression
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science