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We propose a MultiScale AutoEncoder(MSAE) based extreme image compressionframework to offer visually pleasing reconstruction at a very low bitrate. Ourmethod leverages the "priors" at different resolution scale to improve thecompression efficiency, and also employs the generative adversarialnetwork(GAN) with multiscale discriminators to perform the end-to-end trainablerate-distortion optimization. We compare the perceptual quality of ourreconstructions with traditional compression algorithms using High-EfficiencyVideo Coding(HEVC) based Intra Profile and JPEG2000 on the public Cityscapesand ADE20K datasets, demonstrating the significant subjective qualityimprovement.

Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization