Reducing location map in prediction-based difference expansion for reversible image data embedding
Author
Liu, Minglei
Seah, Hock Soon
Zhu, Ce
Lin, Weisi
Tian, Feng
Date of Issue
2011School
School of Computer Engineering
School of Electrical and Electronic Engineering
School of Electrical and Electronic Engineering
Abstract
In this paper, we present a reversible data embedding scheme based on an adaptive edge-directed prediction for images. It is known that the difference expansion is an efficient data embedding method. Since the expansion on a large difference will cause a significant embedding distortion, a location map is usually employed to select small differences for expansion and to avoid overflow/underflow problems caused by expansion. However, location map bits lower payload capacity for data embedding. To reduce the location map, our proposed scheme aims to predict small prediction errors for expansion by using an edge detector. Moreover, to generate a small prediction error for each pixel, an adaptive edge-directed prediction is employed which adapts reasonably well between smooth regions and edge areas. Experimental results show that our proposed data embedding scheme for natural images can achieve a high embedding capacity while keeping the embedding distortion low.
Subject
DRNTU::Engineering::Computer science and engineering
Type
Journal Article
Series/Journal Title
Signal processing
Rights
© 2011 Elsevier B.V.
Collections
http://dx.doi.org/10.1016/j.sigpro.2011.09.028
Get published version (via Digital Object Identifier)