论文标题
DEEPFG：学习图像压缩的细粒度可扩展编码
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
论文作者
论文摘要
可扩展的编码可以适应通道带宽变化，在当今的复杂网络环境中表现良好。但是，现有的可扩展压缩方法面临两个挑战：降低压缩性能和不足的可扩展性。在本文中，我们提出了第一个学到的细颗粒可伸缩图像压缩模型（DEEPFGS），以克服上述两个缺点。具体来说，我们引入了一个功能分离主链，以将图像信息分为基本和可扩展的特征，然后通过信息重排策略通过通道重新分布特征通道。通过这种方式，我们可以通过一通编码生成一个连续可扩展的Bitstream。此外，我们重复使用解码器来降低DEEPFG的参数和计算复杂性。实验表明，我们的DEEPFG在PSNR和MS-SSIM指标中优于所有基于学习的可扩展图像压缩模型和常规可扩展图像编解码器。据我们所知，我们的DEEPFG是对学习的细粒可扩展编码的首次探索，该编码与基于学习的方法相比实现了最好的可扩展性。
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.
