DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
This work addresses scalable image compression for complex network environments, representing an incremental improvement over existing methods.
The paper tackled the challenges of reduced compression performance and insufficient scalability in existing scalable image compression methods by proposing DeepFGS, a learned fine-grained scalable framework that outperforms previous models in PSNR and MS-SSIM metrics.
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. To overcome the above problems, this paper proposes a learned fine-grained scalable image compression framework, namely DeepFGS. 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. For entropy coding, we design a mutual entropy model to fully explore the correlation between the basic and scalable features. In addition, we reuse the decoder to reduce the parameters and computational complexity. Experiments demonstrate that our proposed DeepFGS outperforms previous learning-based scalable image compression models and traditional scalable image codecs in both PSNR and MS-SSIM metrics.