论文标题
实用学习的无损jpeg重新压缩与DCT域中的多级跨通道熵模型
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain
论文作者
论文摘要
JPEG是一种流行的图像压缩方法，由个人，数据中心，云存储和网络文件系统广泛使用。但是，图像压缩的最新进展主要集中在未压缩的图像上，同时忽略了数万亿个已经存在的JPEG图像。为了充分压缩这些JPEG图像，并在需要时无效地将它们恢复回JPEG格式，我们提出了一种基于深度学习的JPEG重新压缩方法，该方法在DCT域上运行，并提出了一个多级跨通道熵模型，以压缩最有用的Y组件。实验表明，与传统的JPEG重新压缩方法（包括Lepton，JPEG XL和CMIX）相比，我们的方法实现了最先进的性能。据我们所知，这是第一种学习的压缩方法，它无损地将JPEG图像转化为更多的存储储存bitstreams。
JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems. However, most recent progress on image compression mainly focuses on uncompressed images while ignoring trillions of already-existing JPEG images. To compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, we propose a deep learning based JPEG recompression method that operates on DCT domain and propose a Multi-Level Cross-Channel Entropy Model to compress the most informative Y component. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this is the first learned compression method that losslessly transcodes JPEG images to more storage-saving bitstreams.
