  Deep Neural Networks trained as image auto-encoders have recently emerged as
a promising direction for advancing the state-of-the-art in image compression.
The key challenge in learning such networks is twofold: To deal with
quantization, and to control the trade-off between reconstruction error
(distortion) and entropy (rate) of the latent image representation. In this
paper, we focus on the latter challenge and propose a new technique to navigate
the rate-distortion trade-off for an image compression auto-encoder. The main
idea is to directly model the entropy of the latent representation by using a
context model: A 3D-CNN which learns a conditional probability model of the
latent distribution of the auto-encoder. During training, the auto-encoder
makes use of the context model to estimate the entropy of its representation,
and the context model is concurrently updated to learn the dependencies between
the symbols in the latent representation. Our experiments show that this
approach, when measured in MS-SSIM, yields a state-of-the-art image compression
system based on a simple convolutional auto-encoder.
