Traditional Transformation Theory Guided Model for Learned Image
  Compression
Zhiyuan Li Chenyang Ge Shun Li
Recently, many deep image compression methods have been proposed and achieved
remarkable performance. However, these methods are dedicated to optimizing the
compression performance and speed at medium and high bitrates, while research
on ultra low bitrates is limited. In this work, we propose a ultra low bitrates
enhanced invertible encoding network guided by traditional transformation
theory, experiments show that our codec outperforms existing methods in both
compression and reconstruction performance. Specifically, we introduce the
Block Discrete Cosine Transformation to model the sparsity of features and
employ traditional Haar transformation to improve the reconstruction
performance of the model without increasing the bitstream cost.