  To enhance image compression performance, recent deep neural network-based
research can be divided into three categories: a learnable codec, a
postprocessing network, and a compact representation network. The learnable
codec has been designed for an end-to-end learning beyond the conventional
compression modules. The postprocessing network increases the quality of
decoded images using an example-based learning. The compact representation
network is learned to reduce the capacity of an input image to reduce the
bitrate while keeping the quality of the decoded image. However, these
approaches are not compatible with the existing codecs or not optimal to
increase the coding efficiency. Specifically, it is difficult to achieve
optimal learning in the previous studies using the compact representation
network, due to the inaccurate consideration of the codecs. In this paper, we
propose a novel standard compatible image compression framework based on
Auxiliary Codec Networks (ACNs). ACNs are designed to imitate image degradation
operations of the existing codec, which delivers more accurate gradients to the
compact representation network. Therefore, the compact representation and the
postprocessing networks can be learned effectively and optimally. We
demonstrate that our proposed framework based on JPEG and High Efficiency Video
Coding (HEVC) standard substantially outperforms existing image compression
algorithms in a standard compatible manner.
