A Novel STDM Watermarking Using Visual Saliency-Based JND Model

Wang, Chunxing; Zhang, Teng; Wan, Wenbo; Han, Xiaoyue; Xu, Meiling

doi:10.3390/info8030103

Cited by 9 publications

(10 citation statements)

References 31 publications

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“…In addition. The work in [37] studied the spread transform dither modulation (STDM) watermarking algorithm based on the visual saliency model and achieved good results. Furthermore, Wang et al studied the JND estimation algorithm based on visual saliency in the wavelet domain [38].…”

Section: Visual Saliency Based Watermark Strength Factormentioning

confidence: 99%

Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

2019

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Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack.

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Section: Visual Saliency Based Watermark Strength Factormentioning

confidence: 99%

Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

2019

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“…Computer scientists have developed numerous computational visual saliency algorithms, which aim at detecting the salient regions in an image. Computational visual saliency models find their applications in a broad spectrum of domains including remote sensing [24], watermarking [25], privacy [26], text detection [27], object recognition [28], multi-camera calibration [29], binocular vision [30], and video coding [31]. Generally, saliency detection techniques are categorized into bottom-up and top-down approaches.…”

Section: Entropy Based Visual Saliency Modelmentioning

confidence: 99%

High Efficiency Video Coding Compliant Perceptual Video Coding Using Entropy Based Visual Saliency Model

Zeeshan

Majid

2019

Entropy

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In past years, several visual saliency algorithms have been proposed to extract salient regions from multimedia content in view of practical applications. Entropy is one of the important measures to extract salient regions, as these regions have high randomness and attract more visual attention. In the context of perceptual video coding (PVC), computational visual saliency models that utilize the charactertistics of the human visual system to improve the compression ratio are of paramount importance. To date, only a few PVC schemes have been reported that use the visual saliency model. In this paper, we conduct the first attempt to utilize entropy based visual saliency models within the high efficiency video coding (HEVC) framework. The visual saliency map generated for each input video frame is optimally thresholded to generate a binary saliency mask. The proposed HEVC compliant PVC scheme adjusts the quantization parameter according to visual saliency relevance at the coding tree unit (CTU) level. Efficient CTU level rate control is achieved by allocating bits to salient and non-salient CTUs by adjusting the quantization parameter values according to their perceptual weighted map. The attention based on information maximization has shown the best performance on newly created ground truth dataset, which is then incorporated in a HEVC framework. An average bitrate reduction of 6 . 57 % is achieved by the proposed HEVC compliant PVC scheme with the same perceptual quality and a nominal increase in coding complexity of 3 . 34 % when compared with HEVC reference software. Moreover, the proposed PVC scheme performs better than other HEVC based PVC schemes when encoded at low data rates.

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“…In 2015, Wan et al [21] proposed a logarithmic STDM watermarking using a VS-based JND model. Recently, Wang et al [22] proposed a novel STDM watermarking using a VS-based JND model. The VS model was employed as a feature to reflect the importance of a local region and compute the final JND map.…”

Section: Introductionmentioning

confidence: 99%

Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

Zhang

Wang

et al. 2019

IEEE Access

Self Cite

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The visual saliency-based just noticeable distortion (VS-JND) model has been widely used in the quantization-based watermarking framework, but it remains a challenge that how to reflect the characteristics of the human visual system (HVS) to the greatest extent in the processing of detecting salient objects. In this paper, given that the HVS has a different perception sensitivity in the different orientation of the image, a compound orientation feature map, which contains the vertical, horizontal, and diagonal information for directional patch extraction, is computed to improve the visual saliency map within just noticeable distortion (JND) profile. First, the DC and three low-frequency coefficients of each block are used to calculate the luminance and texture feature map, respectively. Then, the feature map based on the complex directional feature includes the horizontal, vertical, and diagonal information contained in the image is calculated by three low-frequency coefficients. Finally, we will be able to make a linear fusion of the same three different visual features to get the final VS map, and the JND model is improved according to the new VS model. Later, the new proposed VS-JND model is employed in the quantization watermarking (Q-watermarking) framework. The simulation results show that the proposed watermarking scheme is effective and produces superior robustness compared with existing monochrome image watermarking schemes.

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A Novel STDM Watermarking Using Visual Saliency-Based JND Model

Cited by 9 publications

References 31 publications

Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

High Efficiency Video Coding Compliant Perceptual Video Coding Using Entropy Based Visual Saliency Model

Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

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