Image Compression-Aware Deep Camera ISP Network

Kwang Hyun Uhm, Kyuyeon Choi, Seung Won Jung, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Several recent studies have attempted to fully replace the conventional camera image signal processing (ISP) pipeline with convolutional neural networks (CNNs). However, the previous CNN-based ISPs, simply referred to as ISP-Nets, have not explicitly considered that images have to be lossy-compressed in most cases, especially by the off-the-shelf JPEG. To address this issue, in this paper, we propose a novel compression-aware deep camera ISP learning framework. At first, we introduce a new use case of compression artifacts simulation network (CAS-Net), which operates in the opposite way of commonly used compression artifacts reduction networks. Then, the CAS-Net is connected with an ISP-Net such that the ISP network can be trained with consideration of image compression. Throughout experimental studies, we show that our compression-aware camera ISP network can produce images with a better tradeoff between bit-rate and image quality compared to its compression-agnostic version when the performance is evaluated after JPEG compression.

Original languageEnglish
Pages (from-to)137824-137832
Number of pages9
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT Future Planning under Grant NRF-2020R1F1A1069009, and in part by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea Government (MSIT) under Grant 2019-0-00268, Development of SW Technology for Recognition, Judgment and Path Control Algorithm Verification Simulation and Dataset Generation.

Publisher Copyright:
© 2013 IEEE.


  • Camera ISP
  • compression artifacts
  • convolutional neural network
  • image compression

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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