  Neural image compression (NIC) is a new coding paradigm where coding
capabilities are captured by deep models learned from data. This data-driven
nature enables new potential functionalities. In this paper, we study the
adaptability of codecs to custom domains of interest. We show that NIC codecs
are transferable and that they can be adapted with relatively few target domain
images. However, naive adaptation interferes with the solution optimized for
the original source domain, resulting in forgetting the original coding
capabilities in that domain, and may even break the compatibility with
previously encoded bitstreams. Addressing these problems, we propose Codec
Adaptation without Forgetting (CAwF), a framework that can avoid these problems
by adding a small amount of custom parameters, where the source codec remains
embedded and unchanged during the adaptation process. Experiments demonstrate
its effectiveness and provide useful insights on the characteristics of
catastrophic interference in NIC.
