Improved logarithmic spread transform dither modulation using a robust perceptual model

Wan, Wenbo; Liu, Ju; Sun, Jiande; Gao, Di

doi:10.1007/s11042-015-2853-5

Cited by 18 publications

(33 citation statements)

References 18 publications

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“…The model provides a more precise CSF, and the spatial frequency is used as one of determining factor to estimate the LA and CM effects. In [14], the authors have put forward a DCT-based JND model which extracts the frequency characteristics from the corresponding psychophysical experiment results, and this model is the product of three parts: the base threshold, the masking factors for LA and CM. In this model, there is a new approach to calculate edge strength and edge density.…”

Section: Jnd Estimationmentioning

confidence: 99%

“…However, applying the Kim's model or the model of [15] to the STDM framework exists mismatch problem, which is mainly because the JND value estimated from the watermarked image is distinguished with that from the initial image. The model of [14] is applied into the STDM framework, which can solve the mismatch problem. Nevertheless, the structural characteristics are not considered in this model so that the visual redundancies cannot be estimated well.…”

Section: Jnd Estimationmentioning

confidence: 99%

“…The Canny operator results in a mismatch problem: the CM factor obtained from the watermarking image is inconsistent with that from the host image without any attacks. To settle the mismatch problem, the model of [14] used two AC coefficients to measure the edge strength of a block. Although this method can avoid the edge computation using Canny operator, the texture complexity of a block in the diagonal direction was not considered.…”

Section: B Edge Pixel Densitymentioning

confidence: 99%

See 2 more Smart Citations

Robust Image Watermarking via Perceptual Structural Regularity-based JND Model

Wang¹,

Xu²,

Wan³

et al. 2019

KSII TIIS

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A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed , which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

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Section: Jnd Estimationmentioning

confidence: 99%

Section: Jnd Estimationmentioning

confidence: 99%

Section: B Edge Pixel Densitymentioning

confidence: 99%

See 1 more Smart Citation

Robust Image Watermarking via Perceptual Structural Regularity-based JND Model

Wang¹,

Xu²,

Wan³

et al. 2019

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…In the procedure of watermark detection, the corresponding watermark data can be extracted based on the quantization interval. The quantized-based watermarking method has high capacity, and furthermore, the method is a blind detection scheme, so it has been widely studied in recent years [7][8][9][10][11][12]. A classical quantization watermarking method is the quantization index modulation watermarking method proposed by Chen et al in [7].…”

Section: Introductionmentioning

confidence: 99%

“…This method is robust to gain attacks due to the use of the division function. Wan et al [11] proposed an adaptive spread transform dither modulation watermarking method based on the perceptual visual model. This paper exploited a perceptual just-noticeable distortion model in the discrete cosine transform (DCT) domain.…”

Section: Introductionmentioning

confidence: 99%

Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Liu

Zhao

2018

Information

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To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

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Robust Complementary Dual Image Watermarking in Subbands Derived from the Laplacian Pyramid, Discrete Wavelet Transform, and Directional Filter Bank

Lee

2022

Circuits Syst Signal Process

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Improved logarithmic spread transform dither modulation using a robust perceptual model

Cited by 18 publications

References 18 publications

Robust Image Watermarking via Perceptual Structural Regularity-based JND Model

Robust Image Watermarking via Perceptual Structural Regularity-based JND Model

Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Robust Complementary Dual Image Watermarking in Subbands Derived from the Laplacian Pyramid, Discrete Wavelet Transform, and Directional Filter Bank

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