  This paper presents a set of full-resolution lossy image compression methods
based on neural networks. Each of the architectures we describe can provide
variable compression rates during deployment without requiring retraining of
the network: each network need only be trained once. All of our architectures
consist of a recurrent neural network (RNN)-based encoder and decoder, a
binarizer, and a neural network for entropy coding. We compare RNN types (LSTM,
associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study
"one-shot" versus additive reconstruction architectures and introduce a new
scaled-additive framework. We compare to previous work, showing improvements of
4.3%-8.8% AUC (area under the rate-distortion curve), depending on the
perceptual metric used. As far as we know, this is the first neural network
architecture that is able to outperform JPEG at image compression across most
bitrates on the rate-distortion curve on the Kodak dataset images, with and
without the aid of entropy coding.
