High Precision Error Prediction Algorithm Based on Ridge Regression Predictor for Reversible Data Hiding

An efficient predictor is crucial for high embedding capacity and low image distortion. In this letter, a ridge regression-based high precision error prediction algorithm for reversible data hiding is proposed. The ridge regression is a penalized least-square algorithm, which solves the overfitting...

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Vydáno v:IEEE signal processing letters Ročník 28; s. 1125 - 1129
Hlavní autoři: Wang, Xiaoyu, Wang, Xingyuan, Ma, Bin, Li, Qi, Shi, Yun-Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
adaptive method
Algorithms
Embedding
error prediction
Histograms
Least squares
Pixels
Prediction algorithms
Reactive power
Regression
Reversible data hiding
ridge regression predictor
Signal processing algorithms
Stability criteria
Technological innovation
Training
ISSN:1070-9908, 1558-2361
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Shrnutí:An efficient predictor is crucial for high embedding capacity and low image distortion. In this letter, a ridge regression-based high precision error prediction algorithm for reversible data hiding is proposed. The ridge regression is a penalized least-square algorithm, which solves the overfitting problem of the least-square method. Reversible data hiding based on ridge regression predictor minimizes the residual sum of squares between predicted and target pixels subject to the constraint expressed in terms of the L2-norm. Compared to a least-square-based predictor, the ridge regression-based predictor can obtain more small prediction errors, proving that the proposed method has a higher accuracy. In addition, the eight neighbor pixels of the target pixels and their two different combinations are selected as training and support sets, respectively. This selection scheme further improves the prediction accuracy. Experimental results show that the proposed method outperforms state-of-the-art adaptive reversible data hiding in terms of prediction accuracy and embedding performance.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3080181