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Journal of Electronic Imaging • Open Access

Global motion compensated visual attention-based video watermarking

Paper Abstract

Imperceptibility and robustness are two key but complementary requirements of any watermarking algorithm. Low-strength watermarking yields high imperceptibility but exhibits poor robustness. High-strength watermarking schemes achieve good robustness but often suffer from embedding distortions resulting in poor visual quality in host media. This paper proposes a unique video watermarking algorithm that offers a fine balance between imperceptibility and robustness using motion compensated wavelet-based visual attention model (VAM). The proposed VAM includes spatial cues for visual saliency as well as temporal cues. The spatial modeling uses the spatial wavelet coefficients while the temporal modeling accounts for both local and global motion to arrive at the spatiotemporal VAM for video. The model is then used to develop a video watermarking algorithm, where a two-level watermarking weighting parameter map is generated from the VAM saliency maps using the saliency model and data are embedded into the host image according to the visual attentiveness of each region. By avoiding higher strength watermarking in the visually attentive region, the resulting watermarked video achieves high perceived visual quality while preserving high robustness. The proposed VAM outperforms the state-of-the-art video visual attention methods in joint saliency detection and low computational complexity performance. For the same embedding distortion, the proposed visual attention-based watermarking achieves up to 39% (nonblind) and 22% (blind) improvement in robustness against H.264/AVC compression, compared to existing watermarking methodology that does not use the VAM. The proposed visual attention-based video watermarking results in visual quality similar to that of low-strength watermarking and a robustness similar to those of high-strength watermarking.

Paper Details

Date Published: 20 December 2016
PDF: 16 pages
J. Electron. Imag. 25(6) 061624 doi: 10.1117/1.JEI.25.6.061624
Published in: Journal of Electronic Imaging Volume 25, Issue 6
Show Author Affiliations
Matthew Oakes, The Univ. of Buckingham (United Kingdom)
Deepayan Bhowmik, Sheffield Hallam Univ. (United Kingdom)
Charith Abhayaratne, The Univ. of Sheffield (United Kingdom)


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