  The latest advancements in neural image compression show great potential in
surpassing the rate-distortion performance of conventional standard codecs.
Nevertheless, there exists an indelible domain gap between the datasets
utilized for training (i.e., natural images) and those utilized for inference
(e.g., artistic images). Our proposal involves a low-rank adaptation approach
aimed at addressing the rate-distortion drop observed in out-of-domain
datasets. Specifically, we perform low-rank matrix decomposition to update
certain adaptation parameters of the client's decoder. These updated
parameters, along with image latents, are encoded into a bitstream and
transmitted to the decoder in practical scenarios. Due to the low-rank
constraint imposed on the adaptation parameters, the resulting bit rate
overhead is small. Furthermore, the bit rate allocation of low-rank adaptation
is \emph{non-trivial}, considering the diverse inputs require varying
adaptation bitstreams. We thus introduce a dynamic gating network on top of the
low-rank adaptation method, in order to decide which decoder layer should
employ adaptation. The dynamic adaptation network is optimized end-to-end using
rate-distortion loss. Our proposed method exhibits universality across diverse
image datasets. Extensive results demonstrate that this paradigm significantly
mitigates the domain gap, surpassing non-adaptive methods with an average
BD-rate improvement of approximately $19\%$ across out-of-domain images.
Furthermore, it outperforms the most advanced instance adaptive methods by
roughly $5\%$ BD-rate. Ablation studies confirm our method's ability to
universally enhance various image compression architectures.
