  Deep image compression systems mainly contain four components: encoder,
quantizer, entropy model, and decoder. To optimize these four components, a
joint rate-distortion framework was proposed, and many deep neural
network-based methods achieved great success in image compression. However,
almost all convolutional neural network-based methods treat channel-wise
feature maps equally, reducing the flexibility in handling different types of
information. In this paper, we propose a channel-level variable quantization
network to dynamically allocate more bitrates for significant channels and
withdraw bitrates for negligible channels. Specifically, we propose a variable
quantization controller. It consists of two key components: the channel
importance module, which can dynamically learn the importance of channels
during training, and the splitting-merging module, which can allocate different
bitrates for different channels. We also formulate the quantizer into a
Gaussian mixture model manner. Quantitative and qualitative experiments verify
the effectiveness of the proposed model and demonstrate that our method
achieves superior performance and can produce much better visual
reconstructions.
