Lossless data embedding--new paradigm in digital watermarking
EURASIP Journal on Applied Signal Processing - Emerging applications of multimedia data hiding
A lossless data hiding scheme based on three-pixel block differences
Pattern Recognition
Reversibility improved lossless data hiding
Signal Processing
Reversible data hiding based on histogram modification of pixel differences
IEEE Transactions on Circuits and Systems for Video Technology
Reversible watermarking algorithm using sorting and prediction
IEEE Transactions on Circuits and Systems for Video Technology
DE-based reversible data hiding with improved overflow location map
IEEE Transactions on Circuits and Systems for Video Technology
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Reversible image watermarking using interpolation technique
IEEE Transactions on Information Forensics and Security
The Research of Image Segmentation Based on Improved Neural Network Algorithm
SKG '10 Proceedings of the 2010 Sixth International Conference on Semantics, Knowledge and Grids
Reversible watermark using the difference expansion of a generalized integer transform
IEEE Transactions on Image Processing
Binary Tree-based Generic Demosaicking Algorithm for Multispectral Filter Arrays
IEEE Transactions on Image Processing
Expansion Embedding Techniques for Reversible Watermarking
IEEE Transactions on Image Processing
Reversible data embedding using a difference expansion
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
In this paper, we attempt to construct a novel framework of reversible watermarking. This work is based on the difference-image histogram shift. De-correlation is the core of high capacity data-hiding in histogram-shift techniques. For the sake of higher payload, we choose the down-sample pattern as reference set. For each layer, prediction points are obtained in terms of points from the reference set. The full-resolution image quality reconstructed determines to reversible watermarking performance. When existing the prior knowledge, an effective regression method named extreme learning machine is utilized to estimate missing pixels. It can yield high-quality recovery image. Compared to other better algorithms on state of the art, the proposed method achieves higher capacity gain of watermarked images with the similar distortion.