  The field of neural image compression has witnessed exciting progress as
recently proposed architectures already surpass the established transform
coding based approaches. While, so far, research has mainly focused on
architecture and model improvements, in this work we explore content adaptive
optimization. To this end, we introduce an iterative procedure which adapts the
latent representation to the specific content we wish to compress while keeping
the parameters of the network and the predictive model fixed. Our experiments
show that this allows for an overall increase in rate-distortion performance,
independently of the specific architecture used. Furthermore, we also evaluate
this strategy in the context of adapting a pretrained network to other content
that is different in visual appearance or resolution. Here, our experiments
show that our adaptation strategy can largely close the gap as compared to
models specifically trained for the given content while having the benefit that
no additional data in the form of model parameter updates has to be
transmitted.
