  We propose a new approach to the problem of optimizing autoencoders for lossy
image compression. New media formats, changing hardware technology, as well as
diverse requirements and content types create a need for compression algorithms
which are more flexible than existing codecs. Autoencoders have the potential
to address this need, but are difficult to optimize directly due to the
inherent non-differentiabilty of the compression loss. We here show that
minimal changes to the loss are sufficient to train deep autoencoders
competitive with JPEG 2000 and outperforming recently proposed approaches based
on RNNs. Our network is furthermore computationally efficient thanks to a
sub-pixel architecture, which makes it suitable for high-resolution images.
This is in contrast to previous work on autoencoders for compression using
coarser approximations, shallower architectures, computationally expensive
methods, or focusing on small images.
