Skip to main navigation Skip to search Skip to main content

A blind wavelet-based watermarking with detail-subband coefficients prediction

  • Jing Ming Guo*
  • , Yu Quan Tzeng
  • , Jiann Der Lee
  • *Corresponding author for this work
  • National Taiwan University of Science and Technology
  • Chang Gung University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Recently, the wavelet transform is widely used in multimedia signal processing applications. To provide security solution, the digital watermarking is involved. This study presents a blind wavelet-based watermarking which cooperates with the Human Visual System (HVS) embedding watermarks into detail-subband coefficients. Since the imperceptibility is the most significant issue in watermarking, the approximate band is maintained unchanged, while the other detail subbands are modified to carry information. The perceptual embedded weights for all subbands are determined based on the Just Noticeable Distortion (JND) criterion. The strength of the modification is investigated to provide a compromised result between robustness and image quality. In the decoder, the Least-Mean-Square (LMS) is involved to predict the original detail-subband coefficients and then extract the embedded watermarks. As documented in experimental results, the proposed method provides good robustness and excellent image quality.

Original languageEnglish
Title of host publicationProceedings - 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008
PublisherIEEE Computer Society
Pages1805-1810
Number of pages6
ISBN (Print)9781424417667
DOIs
StatePublished - 2008

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Keywords

  • Digital watermarking
  • Discrete wavelet transform
  • Human visual system
  • Just Noticeable Distortion
  • Least Mean Square

Fingerprint

Dive into the research topics of 'A blind wavelet-based watermarking with detail-subband coefficients prediction'. Together they form a unique fingerprint.

Cite this