Automatic contrast enhancement using reversible data hiding

Suah Kim, Rolf Lussi, Xiaochao Qu, Hyoung Joong Kim

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

36 Citations (Scopus)

Abstract

Automatic image enhancement is increasingly becoming a popular tool for the smartphone environment. The tool automatically enhances the image right after it has been stored to enhance user's experience. But, because enhancements are subjective and dependent on the image, the original image is backed up to provide a recovery option. This requirement inevitably increases the storage requirement. In order to reduce it, a novel automatic contrast enhancement based on reversible data hiding (ACERDH) is proposed. The proposed method mimics the equalization effect observed in the basic contrast enhancement technique called global histogram equalization, while providing reversibility. The experiment visually show improved contrast. Additional experiment was done to compare the embedding capacity with an another reversible data hiding based contrast enhancement technique [4]. The proposed method is fit for automation, while providing data hiding capability and removing the additional storage requirement.

Original languageEnglish
Title of host publication2015 IEEE International Workshop on Information Forensics and Security, WIFS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467368025
DOIs
Publication statusPublished - 2015 Dec 29
EventIEEE International Workshop on Information Forensics and Security, WIFS 2015 - Rome, Italy
Duration: 2015 Nov 162015 Nov 19

Publication series

Name2015 IEEE International Workshop on Information Forensics and Security, WIFS 2015 - Proceedings

Other

OtherIEEE International Workshop on Information Forensics and Security, WIFS 2015
Country/TerritoryItaly
CityRome
Period15/11/1615/11/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications

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