  We describe an end-to-end trainable model for image compression based on
variational autoencoders. The model incorporates a hyperprior to effectively
capture spatial dependencies in the latent representation. This hyperprior
relates to side information, a concept universal to virtually all modern image
codecs, but largely unexplored in image compression using artificial neural
networks (ANNs). Unlike existing autoencoder compression methods, our model
trains a complex prior jointly with the underlying autoencoder. We demonstrate
that this model leads to state-of-the-art image compression when measuring
visual quality using the popular MS-SSIM index, and yields rate-distortion
performance surpassing published ANN-based methods when evaluated using a more
traditional metric based on squared error (PSNR). Furthermore, we provide a
qualitative comparison of models trained for different distortion metrics.
