Imperceptibility and robustness are two complementary but fundamental requirements of any watermarking algorithm. Low strength watermarking yields high imperceptibility but exhibits poor robustness. High strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host media. If distortion due to high strength watermarking can avoid visually attentive regions, such distortions are unlikely to be noticeable to any viewer. In this paper, we exploit this concept and propose a novel visual attention-based highly robust image watermarking methodology by embedding lower and higher strength watermarks in visually salient and non-salient regions, respectively. A new low complexity wavelet domain visual attention model is proposed that allows us to design new robust watermarking algorithms. The proposed new saliency model outperforms the state-of-the-art method in joint saliency detection and low computational complexity performances. In evaluating watermarking performances, the proposed blind and non-blind algorithms exhibit increased robustness to various natural image processing and filtering attacks with minimal or no effect on image quality, as verified by both subjective and objective visual quality evaluation. Up to 25% and 40% improvement against JPEG2000 compression and common filtering attacks, respectively, are reported against the existing algorithms that do not use a visual attention model.
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.
Abstract. The computation of visual attention is an exhaustive procedure to locate conspicuous regions within a frame, which contrast with the surrounding background. In this paper we propose a unique algorithm to estimate visual saliency in the compressed domain using intra-coded frames from High Efficiency Video Coding (HEVC) encoded video sequences. By exclusively combining data obtained from the coding unit structure, intra mode block predictions and the residual data, a visual saliency approximation is obtained. The proposed model can accurately detect salient regions without the need to fully decode the HEVC bitstream. Experimental results show the proposed algorithm compares positively against multiple methods in the literature, highlighting accurate saliency detection with minimal time additions to the video coding computation. The new methodology can provide aid to a wide variety of fields such as advertising, watermarking, video editing and spatial-temporal adaptation.
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