DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
This work addresses scalable image compression for complex network environments, presenting a novel approach with incremental improvements in fine-grained scalability.
The paper tackles the problem of reduced compression performance and insufficient scalability in existing scalable image compression methods by proposing DeepFGS, a learned fine-grained scalable model that outperforms all learning-based and conventional scalable codecs in PSNR and MS-SSIM metrics.
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, the existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. In this paper, we propose the first learned fine-grained scalable image compression model (DeepFGS) to overcome the above two shortcomings. Specifically, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. In this way, we can generate a continuously scalable bitstream via one-pass encoding. In addition, we reuse the decoder to reduce the parameters and computational complexity of DeepFGS. Experiments demonstrate that our DeepFGS outperforms all learning-based scalable image compression models and conventional scalable image codecs in PSNR and MS-SSIM metrics. To the best of our knowledge, our DeepFGS is the first exploration of learned fine-grained scalable coding, which achieves the finest scalability compared with learning-based methods.