Vol.73, No.2, 2022, pp.3613-3628, doi:10.32604/cmc.2022.030372
OPEN ACCESS
ARTICLE
Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling
  • Jiaohua Qin1,*, Zhibin He1, Xuyu Xiang1, Neal N. Xiong2
1 College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410004, China
2 Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, 74464, OK, USA
* Corresponding Author: Jiaohua Qin. Email:
Received 24 March 2022; Accepted 07 May 2022; Issue published 16 June 2022
Abstract
Recently, reversible data hiding in encrypted images (RDHEI) based on pixel prediction has been a hot topic. However, existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction, and the pixel labeling scheme is inflexible. To solve these problems, this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling. First, we design an adaptive gradient prediction (AGP), which uses eight adjacent pixels and combines four scanning methods (i.e., horizontal, vertical, diagonal, and diagonal) for prediction. AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel, which improves the prediction ability of the predictor for complex images. At the same time, we adopt an adaptive huffman coding labeling scheme, which can adaptively generate huffman codes for labeling according to different images, effectively improving the scheme’s embedding performance on the dataset. The experimental results show that the algorithm has a higher embedding rate. The embedding rate on the test image Jetplane is 4.2102 bpp, and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.
Keywords
Reversible data hiding; adaptive gradient prediction; huffman coding; embedding capacity
Cite This Article
J. Qin, Z. He, X. Xiang and N. N. Xiong, "Reversible data hiding in encrypted images based on adaptive prediction and labeling," Computers, Materials & Continua, vol. 73, no.2, pp. 3613–3628, 2022.
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