Learned Scalable Image Compression with Bidirectional Context Disentanglement Network
This work addresses scalable image compression for applications requiring variable bitrates, offering incremental improvements over existing deep learning methods.
The paper tackles scalable image compression by proposing a Bidirectional Context Disentanglement Network (BCD-Net) that uses bit-plane decomposition and bidirectional flows to disentangle contextual information, resulting in improved performance over state-of-the-art DNN-based methods in PSNR and MS-SSIM metrics.
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.