Logarithmic STDM watermarking using visual saliency‐based JND model

Wan, Wenbo; Liu, Ju; Sun, Jiande; Ge, Chuan; Nie, Xiushan

doi:10.1049/el.2014.4329

Cited by 15 publications

(9 citation statements)

References 9 publications

(18 reference statements)

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“…Three techniques serve as the ground work while developing our proposed scheme. The works in [20] and [22] employed the bottom-up features of luminance and texture only. The basic luminance and texture features lead to acceptable accuracy in VS model, however, it is too simple to achieve more accurate visual saliency.…”

Section: Proposed Visual Saliency Modelmentioning

confidence: 99%

“…The work followed the spectral residual [21] to estimate the saliency map. Moreover, Wan et al [22] utilized the luminance and texture features to obtain the saliency, which can be associated with the Watson's JND model. The obtained saliency-induced JND model was then used to adjust the watermarking quantization step.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, there are still two key issues to be addressed: one is how to improve the overall saliency detection performance. The works in [20] and [22] employed the bottom-up features of luminance and texture only. However, it is too simple to achieve more accurate visual saliency.…”

Section: Introductionmentioning

confidence: 99%

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Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Wan

Wang

et al. 2019

IEEE Access

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Visual saliency (VS) is an important mechanism for defining which areas of an image will attract more attention of the HVS. Thus, VS can be employed to weight the just noticeable difference (JND) with different attention levels. Some VS-based JND profiles have been proposed in the DCT domain, which used the bottom-up features, such as luminance and texture only. Recently, the research about saliency detection has shown that a better saliency model considering both bottom-up features and top-down features will lead to a significant improvement for the overall saliency detection performance. In this paper, we propose a novel two-layer VS-induced JND profile, which is composed of the bottom-up features and the top-down feature extracted from DCT blocks in the transformed domain. In this model, the luminance and texture features are adapted to calculate the bottom-up features maps, while the top-down feature of focus is used to guide the generation of final salient regions since the camerapersons are used to facilitate the attention regions in focus. The proposed two-layer saliency-induced JND model is further applied to modulate the quantization step in the watermarking framework, which can make full use of its individual merits to achieve a better tradeoff between fidelity and robustness. The experimental results show that the proposed scheme has superior performance than the previous watermarking schemes.

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Section: Proposed Visual Saliency Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Wan

Wang

et al. 2019

IEEE Access

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“…After that, Niu et al [20] proposed utilizing visual attention to modulate JND profile and guide watermark embedding. 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.…”

Section: Introductionmentioning

confidence: 99%

Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

Zhang

Wang

et al. 2019

IEEE Access

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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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“…When the distortion is less than the human sensitivity threshold, the human eye is imperceptible [3]. In recent years, the JND model has received wide attention in the aspects of video image encoding [4] [5], digital watermarking [6], image quality evaluation [7] and so on. At present, several JND models have been proposed: the JND model based on pixel domain and the JND model based on transform domain.…”

Section: Introductionmentioning

confidence: 99%

A Perceptual Video Coding Based on JND Model

Yi¹,

Fan²,

Shi³

2018

JCC

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In view of the fact that the current high efficiency video coding standard does not consider the characteristics of human vision, this paper proposes a perceptual video coding algorithm based on the just noticeable distortion model (JND). The adjusted JND model is combined into the transformation quantization process in high efficiency video coding (HEVC) to remove more visual redundancy and maintain compatibility. First of all, we design the JND model based on pixel domain and transform domain respectively, and the pixel domain model can give the JND threshold more intuitively on the pixel. The transform domain model introduces the contrast sensitive function into the model, making the threshold estimation more precise. Secondly, the proposed JND model is embedded in the HEVC video coding framework. For the transformation skip mode (TSM) in HEVC, we adopt the existing pixel domain called nonlinear additively model (NAMM). For the non-transformation skip mode (non-TSM) in HEVC, we use transform domain JND model to further reduce visual redundancy. The simulation results show that in the case of the same visual subjective quality, the algorithm can save more bitrates.

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Logarithmic STDM watermarking using visual saliency‐based JND model

Cited by 15 publications

References 9 publications

Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

A Perceptual Video Coding Based on JND Model

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