  Deep image compression performs better than conventional codecs, such as
JPEG, on natural images. However, deep image compression is learning-based and
encounters a problem: the compression performance deteriorates significantly
for out-of-domain images. In this study, we highlight this problem and address
a novel task: universal deep image compression. This task aims to compress
images belonging to arbitrary domains, such as natural images, line drawings,
and comics. To address this problem, we propose a content-adaptive optimization
framework; this framework uses a pre-trained compression model and adapts the
model to a target image during compression. Adapters are inserted into the
decoder of the model. For each input image, our framework optimizes the latent
representation extracted by the encoder and the adapter parameters in terms of
rate-distortion. The adapter parameters are additionally transmitted per image.
For the experiments, a benchmark dataset containing uncompressed images of four
domains (natural images, line drawings, comics, and vector arts) is constructed
and the proposed universal deep compression is evaluated. Finally, the proposed
model is compared with non-adaptive and existing adaptive compression models.
The comparison reveals that the proposed model outperforms these. The code and
dataset are publicly available at https://github.com/kktsubota/universal-dic.
