Image quality assessment in reversible data hiding with contrast enhancement

Hao Tian Wu, Shaohua Tang, Yun Qing Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, image quality assessment (IQA) in reversible data hiding with contrast enhancement (RDH-CE) is studied. Firstly, the schemes of RDH-CE are reviewed, with which image contrast can be enhanced without any information loss. Secondly, the limitations of using the peak signal-to-noise ratio (PSNR) to indicate image quality in the scenario of RDH-CE are discussed. Subsequently, three no-reference IQA metrics and four metrics specially designed for contrast-changed images are adopted, in addition to PSNR and structural similarity (SSIM) index. By using these metrics, the evaluation results on the contrast-enhanced images generated with two RDH-CE schemes are obtained and compared. The experimental results have shown that the no-reference IQA metrics, the blind/referenceless image spatial quality evaluator (BRISQUE) for instance, are more suitable than PSNR and SSIM index for the images that have been enhanced by the RDH-CE schemes. Furthermore, how to use the suitable IQA metrics has been discussed for performance evaluation of RDH-CE schemes.

Original languageEnglish (US)
Title of host publicationDigital Forensics and Watermarking - 16th International Workshop, IWDW 2017, Proceedings
EditorsYun-Qing Shi, Hyoung Joong Kim, Christian Kraetzer, Jana Dittmann
PublisherSpringer Verlag
Pages290-302
Number of pages13
ISBN (Print)9783319641843
DOIs
StatePublished - 2017
Event16th International Workshop on Digital Forensics and Watermarking, IWDW 2017 - Magdeburg, Germany
Duration: Aug 23 2017Aug 25 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10431 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Workshop on Digital Forensics and Watermarking, IWDW 2017
CountryGermany
CityMagdeburg
Period8/23/178/25/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Contrast enhancement
  • Image quality assessment
  • Reversible data hiding
  • Visual quality

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