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Spatial-Perceptual Embedding with Robust Just Noticeable Difference Model for Color Image Watermarking

Author

Listed:
  • Kai Zhou

    (School of Information and Engineering, Shandong Normal University, Jinan 250014, China)

  • Yunming Zhang

    (School of Information and Engineering, Shandong Normal University, Jinan 250014, China)

  • Jing Li

    (School of Mechanical and Electrical Engineering, Shandong Management University, Jinan 250014, China)

  • Yantong Zhan

    (School of Information and Engineering, Shandong Normal University, Jinan 250014, China)

  • Wenbo Wan

    (School of Information and Engineering, Shandong Normal University, Jinan 250014, China)

Abstract

In the robust image watermarking framework, watermarks are usually embedded in the direct current (DC) coefficients in discrete cosine transform (DCT) domain, since the DC coefficients have a larger perceptual capacity than any alternating current (AC) coefficients. However, DC coefficients are also excluded from watermark embedding with the consideration of avoiding block artifacts in watermarked images. Studies on human vision suggest that perceptual characteristics can achieve better image fidelity. With this perspective, we propose a novel spatial–perceptual embedding for a color image watermarking algorithm that includes the robust just-noticeable difference (JND) guidance. The logarithmic transform function is used for quantization embedding. Meanwhile, an adaptive quantization step is modeled by incorporating the partial AC coefficients. The novelty and effectiveness of the proposed framework are supported by JND perceptual guidance for spatial pixels. Experiments validate that the proposed watermarking algorithm produces a significantly better performance.

Suggested Citation

  • Kai Zhou & Yunming Zhang & Jing Li & Yantong Zhan & Wenbo Wan, 2020. "Spatial-Perceptual Embedding with Robust Just Noticeable Difference Model for Color Image Watermarking," Mathematics, MDPI, vol. 8(9), pages 1-17, September.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1506-:d:408924
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    Cited by:

    1. Tongyuan Huang & Jia Xu & Yuling Yang & Baoru Han, 2022. "Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-19, April.

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