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.

本文研究了使用深度学习模型从数据中学习编码能力的神经网络图像压缩（NIC）的可适应性，提出了一种避免遗忘原始编码能力和维持先前编码比特流兼容性的新框架 Codec Adaptation without Forgetting（CAwF），并展示了其在实验中的有效性和关于 NIC 中灾难性干扰特性的有用见解。