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Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

Hengfu Yang1,2,*, Mingfang Jiang1,2, Zhichen Gao3

1 School of Computer Science, Hunan First Normal University, Changsha, 410205, China
2 Hunan Provincial Key Laboratory of Informationization Technology for Basic Education, Changsha, 410205, China
3 Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, NY 11794, USA

* Corresponding Author: Hengfu Yang. Email: email

Computers, Materials & Continua 2023, 74(3), 5173-5189. https://doi.org/10.32604/cmc.2023.034819

Abstract

Few previous Reversible Visible Watermarking (RVW) schemes have both good transparency and watermark visibility. An adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding (BTC) compressed domain, called TVB-RVW is proposed in this paper. A new mean image estimation method for BTC-compressed images is first developed with the help of Total Variation. Then, a visual perception factor computation model is devised by fusing texture and luminance characteristics. An adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC domain. Moreover, a lossless embedding method of the encrypted visible watermark is exploited to deter illegal watermark removal. The visible watermark can be removed since the visual perception factor and the estimated mean image remain unchanged before and after watermark embedding. Extensive experiments validate the superiority of the proposed algorithm over previous RVW schemes in BTC in terms of the visual quality of watermarked images and watermark visibility, and it can achieve a good balance between transparency and watermark visibility.

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Cite This Article

H. Yang, M. Jiang and Z. Gao, "Adaptive reversible visible watermarking based on total variation for btc-compressed images," Computers, Materials & Continua, vol. 74, no.3, pp. 5173–5189, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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