ULcompress: A Unified low bit-rate image Compression Framework via Invertible Image Representation

In this paper, we propose a unified low bit-rate image compression framework, namely ULCompress, via invertible image representation. The proposed framework is composed of two important modules, including an invertible image rescaling (IIR) module and a compressed quality enhancement (CQE) module. The role of IIR module is to learn a compression-friendly low-resolution (LR) image from the high-resolution (HR) image. Instead of the HR image, we compress the LR image to save the bit-rates. The compression codecs can be any existing codecs. After compression, we propose a CQE module to enhance the quality of the compressed LR image, which is then sent back to the IIR module to restore the original HR image. The network architecture of IIR module is specially designed to ensure the invertibility of LR and HR images, i.e., the downsampling and upsampling processes are invertible. The CQE module works as a buffer between IIR module and the codec, which plays an important role in improving the compatibility of our framework. Experimental results show that our ULCompress is compatible with both standard and learning-based codecs, and is able to significantly improve their performance at low bit-rates.