  We propose a MultiScale AutoEncoder(MSAE) based extreme image compression
framework to offer visually pleasing reconstruction at a very low bitrate. Our
method leverages the "priors" at different resolution scale to improve the
compression efficiency, and also employs the generative adversarial
network(GAN) with multiscale discriminators to perform the end-to-end trainable
rate-distortion optimization. We compare the perceptual quality of our
reconstructions with traditional compression algorithms using High-Efficiency
Video Coding(HEVC) based Intra Profile and JPEG2000 on the public Cityscapes
and ADE20K datasets, demonstrating the significant subjective quality
improvement.
