By Topic

Model Order Selection in Reversible Image Watermarking

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ming Chen ; School of Computer Science and Engineering, Beihang University, Beijing, China ; Zhenyong Chen ; Xiao Zeng ; Zhang Xiong

Digital watermarking is becoming increasingly important in a large number of applications such as copyright protection, content authentication, and document annotation. Thanks to its capability of exactly recovering the original host, reversible image watermarking, a kind of digital watermarking, is favored in fields sensitive to image quality like military and medical imaging. This paper presents theoretical examination and experimental analysis of model order selection in reversible image watermarking. It involves two modeling tools: prediction and context modeling. Classic prediction models are compared and evaluated using specially derived criteria for reversible image watermarking. Among them, the CALIC, a tool combining the Gradient-Adjusted Prediction with a context modeling, stands out as the best by providing the most competitive model-fitness with relatively low complexity. In addition, full context prediction, a model unique to reversible image watermarking, is also discussed. By exploiting redundancy to greater extent, it achieves highly fitted modeling at a very low order. Experimental results demonstrate that it is capable of providing even better performance than the CALIC with only negligible computation.

Published in:

IEEE Journal of Selected Topics in Signal Processing  (Volume:4 ,  Issue: 3 )