  With the development of deep learning techniques, the combination of deep
learning with image compression has drawn lots of attention. Recently, learned
image compression methods had exceeded their classical counterparts in terms of
rate-distortion performance. However, continuous rate adaptation remains an
open question. Some learned image compression methods use multiple networks for
multiple rates, while others use one single model at the expense of
computational complexity increase and performance degradation. In this paper,
we propose a continuously rate adjustable learned image compression framework,
Asymmetric Gained Variational Autoencoder (AG-VAE). AG-VAE utilizes a pair of
gain units to achieve discrete rate adaptation in one single model with a
negligible additional computation. Then, by using exponential interpolation,
continuous rate adaptation is achieved without compromising performance.
Besides, we propose the asymmetric Gaussian entropy model for more accurate
entropy estimation. Exhaustive experiments show that our method achieves
comparable quantitative performance with SOTA learned image compression methods
and better qualitative performance than classical image codecs. In the ablation
study, we confirm the usefulness and superiority of gain units and the
asymmetric Gaussian entropy model.
