Enhanced Standard Compatible Image Compression Framework Based on Auxiliary Codec Networks

Hanbin Son, Taeoh Kim, Hyeongmin Lee, Sangyoun Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)664-677
Number of pages14
JournalIEEE Transactions on Image Processing
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) through the Korean Government and the Ministry of Science and ICT (MSIT) under Grant 2021-0-00172 (the development of human re-identification and masked face recognition based on CCTV camera) and under Grant 2016-0-00197

Publisher Copyright:
© 1992-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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