  This work addresses two major issues of end-to-end learned image compression
(LIC) based on deep neural networks: variable-rate learning where separate
networks are required to generate compressed images with varying qualities, and
the train-test mismatch between differentiable approximate quantization and
true hard quantization. We introduce an online meta-learning (OML) setting for
LIC, which combines ideas from meta learning and online learning in the
conditional variational auto-encoder (CVAE) framework. By treating the
conditional variables as meta parameters and treating the generated conditional
features as meta priors, the desired reconstruction can be controlled by the
meta parameters to accommodate compression with variable qualities. The online
learning framework is used to update the meta parameters so that the
conditional reconstruction is adaptively tuned for the current image. Through
the OML mechanism, the meta parameters can be effectively updated through SGD.
The conditional reconstruction is directly based on the quantized latent
representation in the decoder network, and therefore helps to bridge the gap
between the training estimation and true quantized latent distribution.
Experiments demonstrate that our OML approach can be flexibly applied to
different state-of-the-art LIC methods to achieve additional performance
improvements with little computation and transmission overhead.
