Reversible watermarking is a kind of digital watermarking which is able to recover the original image exactly as well as extracting hidden message. Many algorithms have aimed at lower image distortion in higher embedding capacity. In the reversible data hiding, the role of efficient predictors is crucial. Recently, adaptive predictors using least square approach have been proposed to overcome the limitation of the fixed predictors. This paper proposes a novel reversible data hiding algorithm using least square predictor via least absolute shrinkage and selection operator (LASSO). This predictor is dynamic in nature rather than fixed. Experimental results show that the proposed method outperforms the previous methods including some algorithms which are based on the least square predictors.
A series of reversible watermarking technologies have been proposed to increase embedding capacity and the quality of the watermarked image simultaneously. The major skills include difference expansion, histogram shifting, and optimizing embedding order. In this paper, an accurate predictor is proposed to enhance the difference expansion. An efficient sorter is also suggested to find a more desirable embedding order. The payload is differently distributed into two sub-images, split like a chessboard pattern, for better watermarked image quality. Simulation results of the accurate prediction and sorter based on the payload balancing method yield generally better performance over previous methods. The gap is wide, in particular, in low payload for natural images. The peak signal-to-noise ratio improvement is around 2 dB in low payload ranges.
-Reversible image watermarking, a type of digital data hiding, is capable of recovering the original image and extracting the hidden message with precision. A number of reversible algorithms have been proposed to achieve a high embedding capacity and a low distortion. While numerous algorithms for the achievement of a favorable performance regarding a small embedding capacity exist, the main goal of this paper is the achievement of a more favorable performance regarding a larger embedding capacity and a lower distortion. This paper therefore proposes a reversible data hiding algorithm for which a novel piecewise 2D auto-regression (P2AR) predictor that is based on a rhombus-embedding scheme is used. In addition, a minimum description length (MDL) approach is applied to remove the outlier pixels from a training set so that the effect of a multiple linear regression can be maximized. The experiment results demonstrate that the performance of the proposed method is superior to those of previous methods.
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