Recent deep neural network-based research to enhance image compression performance can be divided into three categories: learnable codecs, postprocessing networks, and compact representation networks. The learnable codec has been designed for end-to-end learning beyond the conventional compression modules. The postprocessing network increases the quality of decoded images using example-based learning. The compact representation network is learned to reduce the capacity of an input image, reducing the bit rate while maintaining the quality of the decoded image. However, these approaches are not compatible with existing codecs or are not optimal for increasing coding efficiency. Specifically, it is difficult to achieve optimal learning in previous studies using a 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). In addition, ACNs are designed to imitate image degradation operations of the existing codec, which delivers more accurate gradients to the compact representation network. Therefore, compact representation and postprocessing networks can be learned effectively and optimally. We demonstrate that the proposed framework based on the JPEG and High Efficiency Video Coding standard substantially outperforms existing image compression algorithms in a standard compatible manner.