1,016 research outputs found
Global motion compensated visual attention-based video watermarking
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
Unified Image and Video Saliency Modeling
Visual saliency modeling for images and videos is treated as two independent
tasks in recent computer vision literature. While image saliency modeling is a
well-studied problem and progress on benchmarks like SALICON and MIT300 is
slowing, video saliency models have shown rapid gains on the recent DHF1K
benchmark. Here, we take a step back and ask: Can image and video saliency
modeling be approached via a unified model, with mutual benefit? We identify
different sources of domain shift between image and video saliency data and
between different video saliency datasets as a key challenge for effective
joint modelling. To address this we propose four novel domain adaptation
techniques - Domain-Adaptive Priors, Domain-Adaptive Fusion, Domain-Adaptive
Smoothing and Bypass-RNN - in addition to an improved formulation of learned
Gaussian priors. We integrate these techniques into a simple and lightweight
encoder-RNN-decoder-style network, UNISAL, and train it jointly with image and
video saliency data. We evaluate our method on the video saliency datasets
DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and
MIT300. With one set of parameters, UNISAL achieves state-of-the-art
performance on all video saliency datasets and is on par with the
state-of-the-art for image saliency datasets, despite faster runtime and a 5 to
20-fold smaller model size compared to all competing deep methods. We provide
retrospective analyses and ablation studies which confirm the importance of the
domain shift modeling. The code is available at
https://github.com/rdroste/unisalComment: Presented at the European Conference on Computer Vision (ECCV) 2020.
R. Droste and J. Jiao contributed equally to this work. v3: Updated Fig. 5a)
and added new MTI300 benchmark results to supp. materia
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