JPEG Robust Invertible Grayscale

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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  • Kunlin Liu
  • Dongdong Chen
  • Weiming Zhang
  • Hang Zhou
  • Jie Zhang
  • Wenbo Zhou
  • Nenghai Yu

Related Research Unit(s)


Original languageEnglish
Pages (from-to)4403-4417
Journal / PublicationIEEE Transactions on Visualization and Computer Graphics
Issue number12
Online published11 Jun 2021
Publication statusPublished - 1 Dec 2022


Invertible grayscale is a special kind of grayscale from which the original color can be recovered. Given an input color image, this seminal work tries to hide the color information into its grayscale counterpart while making it hard to recognize any anomalies. This powerful functionality is enabled by training a hiding sub-network and restoring sub-network in an end-to-end way. Despite its expressive results, two key limitations exist: 1) The restored color image often suffers from some noticeable visual artifacts in the smooth regions. 2) It is very sensitive to JPEG compression, i.e., the original color information cannot be well recovered once the intermediate grayscale image is compressed by JPEG. To overcome these two limitations, this paper introduces adversarial training and JPEG simulator respectively. Specifically, two auxiliary adversarial networks are incorporated to make the intermediate grayscale images and final restored color images indistinguishable from normal grayscale and color images. And the JPEG simulator is utilized to simulate real JPEG compression during the online training so that the hiding and restoring sub-networks can automatically learn to be JPEG robust. Extensive experiments demonstrate that the proposed method is superior to the original invertible grayscale work both qualitatively and quantitatively while ensuring the JPEG robustness. We further show that the proposed framework can be applied under different types of grayscale constraints and achieve excellent results.

Research Area(s)

  • Invertible Grayscale, Adversarial Training, JPEG Robust

Citation Format(s)

JPEG Robust Invertible Grayscale. / Liu, Kunlin; Chen, Dongdong; Liao, Jing et al.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 28, No. 12, 01.12.2022, p. 4403-4417.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review