  Recently, Learning-based image compression has reached comparable performance
with traditional image codecs(such as JPEG, BPG, WebP). However, computational
complexity and rate flexibility are still two major challenges for its
practical deployment. To tackle these problems, this paper proposes two
universal modules named Energy-based Channel Gating(ECG) and Bit-rate
Modulator(BM), which can be directly embedded into existing end-to-end image
compression models. ECG uses dynamic pruning to reduce FLOPs for more than 50\%
in convolution layers, and a BM pair can modulate the latent representation to
control the bit-rate in a channel-wise manner. By implementing these two
modules, existing learning-based image codecs can obtain ability to output
arbitrary bit-rate with a single model and reduced computation.
