Toward Optimal Prediction Error Expansion-Based Reversible Image Watermarking

Reversible image watermarking is a technique that allows the cover image to remain unmodified after watermark extraction. Prediction error expansion-based schemes are currently the most efficient and widely used class of reversible image watermarking techniques. In this paper, first, we prove that t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2020-08, Vol.30 (8), p.2377-2390
Main Authors: Roy, Aniket, Chakraborty, Rajat Subhra
Format: Article
Language:English
Subjects:
Business metrics
Computational complexity
Distortion
Estimation
Histograms
Image coding
integer linear programming
Integer programming
Linear prediction
Linear programming
Mathematical analysis
Measurement
Optimization
prediction error expansion
reversible image watermarking
Watermarking
Wiener filtering
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reversible image watermarking is a technique that allows the cover image to remain unmodified after watermark extraction. Prediction error expansion-based schemes are currently the most efficient and widely used class of reversible image watermarking techniques. In this paper, first, we prove that the bounded capacity distortion minimization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision version of the problem is NP-complete. Then, we prove that the dual problem of bounded distortion capacity maximization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision problem is NP-complete. Furthermore, taking advantage of the integer linear programming formulations of the optimization problems, we find the optimal performance metric values for a given image, using concepts from the optimal linear prediction theory. Our technique allows the calculation of these performance metric limit without assuming any particular prediction scheme. The experimental results for several common benchmark images are consistent with the calculated performance limits validate our approach.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2019.2911042