  In this paper, we propose a learned scalable/progressive image compression
scheme based on deep neural networks (DNN), named Bidirectional Context
Disentanglement Network (BCD-Net). For learning hierarchical representations,
we first adopt bit-plane decomposition to decompose the information coarsely
before the deep-learning-based transformation. However, the information carried
by different bit-planes is not only unequal in entropy but also of different
importance for reconstruction. We thus take the hidden features corresponding
to different bit-planes as the context and design a network topology with
bidirectional flows to disentangle the contextual information for more
effective compressed representations. Our proposed scheme enables us to obtain
the compressed codes with scalable rates via a one-pass encoding-decoding.
Experiment results demonstrate that our proposed model outperforms the
state-of-the-art DNN-based scalable image compression methods in both PSNR and
MS-SSIM metrics. In addition, our proposed model achieves higher performance in
MS-SSIM metric than conventional scalable image codecs. Effectiveness of our
technical components is also verified through sufficient ablation experiments.
