  The leading approach for image compression with artificial neural networks
(ANNs) is to learn a nonlinear transform and a fixed entropy model that are
optimized for rate-distortion performance. We show that this approach can be
significantly improved by incorporating spatially local, image-dependent
entropy models. The key insight is that existing ANN-based methods learn an
entropy model that is shared between the encoder and decoder, but they do not
transmit any side information that would allow the model to adapt to the
structure of a specific image. We present a method for augmenting ANN-based
image coders with image-dependent side information that leads to a 17.8% rate
reduction over a state-of-the-art ANN-based baseline model on a standard
evaluation set, and 70-98% reductions on images with low visual complexity that
are poorly captured by a fixed, global entropy model.
