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Enhancing color image watermarking via fast quaternion Schur decomposition: a high-quality blind approach

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Abstract

In the era of digital multimedia, robust and invisible image watermarking is paramount for copyright protection. Here, we propose a blind watermarking algorithm utilizing fast quaternion Schur decomposition, which uses the intrinsic correlations between color channels and the technique of quantization index modulation to embed and extract watermark information. Our approach innovatively integrates quaternion mathematical tools with an algebraic structure-preserving algorithm, significantly enhancing algorithmic efficiency. Experimental results have shown that the watermark invisibility is above 38 dB, and SSIM is above 0.95. The robustness under conventional attacks has an average NC value above 0.96, and under geometric attacks, the average NC value is above 0.90. The proposed approach has outstanding invisibility and robustness against a spectrum of attacks, while also excelling in real-time performance, embedding capacity, and security, setting a new benchmark for color image watermarking solutions. The related open-sources availability: https://github.com/YunfeiQiu/TVCJ_FQSD

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Funding

The work was supported by the National Natural Science Foundations of China (No. 61771231), and the Innovation Project for graduate students of Ludong University (No. IPGS2024-086).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Ludong University, Yantai, 264025, China

    Yunfei Qiu, Shuai Jiao & Qingtang Su

Authors
  1. Yunfei Qiu
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  2. Shuai Jiao
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  3. Qingtang Su
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Contributions

Yunfei Qiu contributed to Writing—original draft, Methodology, Data curation, and Conceptualization. Shuai Jiao contributed to Writing—review. Qingtang Su contributed to review & editing, Supervision, Project administration, and Funding acquisition.

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Correspondence to Qingtang Su.

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Qiu, Y., Jiao, S. & Su, Q. Enhancing color image watermarking via fast quaternion Schur decomposition: a high-quality blind approach. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03674-y

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