Visual saliency’s modulatory effect on just noticeable distortion profile and its application in image watermarking

Niu, Yaqing; Kyan, Matthew; Ma, Lin; Beghdadi, Azeddine; Krishnan, Sridhar

doi:10.1016/j.image.2012.07.009

Cited by 28 publications

(24 citation statements)

References 35 publications

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“…Then, the saliency map is used to modulate the JND model according to the nonlinear saliency modulation function. Our method in the paper is different from the Niu method [7] in three aspects. First, our various transformations are performed in the wavelet domain, and the Niu method is performed in the DCT domain.…”

Section: Introductionmentioning

confidence: 94%

“…In the existing literature, there is very little with a combination of visual saliency and the JND model. The more prominent methods are proposed in [6,7]. The JND thresholds of the methods in both of the two documents are all tuned by a fixed set of linear significance adjustment functions.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Reference [8] proposed a saliency-modulated JND model in the discrete cosine transform (DCT) domain, in which the JND threshold is determined by the DCT-based JND model whose size is adjusted by two nonlinear modulation functions for the visual significance of a given image pixel. The results of [8] show that the method performs better than both [6,7]. The effect of visual attention on visual acuity is represented by a mathematical expression that is a perceptual quality significance map (PQSM).…”

Section: Introductionmentioning

confidence: 99%

“…JND refers to the threshold of visual sensitivity within the visual range, where the position and spatial extent within the visual range of interest are represented by the visual significance. Thus, this gives us inspiration to consider modulatory effects in JND thresholds [6,7]. A new JND model with visual saliency modulation is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Visual Saliency Based Just Noticeable Difference Estimation in DWT Domain

et al. 2018

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Abstract:It has been known that human visual systems (HVSs) can be applied to describe the underlying masking properties for the image processing. In general, HVS can only perceive small changes in a scene when they are greater than the just noticeable distortion (JND) threshold. Recently, the cognitive resources of huma visual attention mechanisms are limited, which can not concentrate on all stimuli. To be specific, only more important stimuli will react from the mechanisms. When it comes to visual attention mechanisms, we need to introduce the visual saliency to model the human perception more accurately. In this paper, we presents a new wavelet-based JND estimation method that takes into account the interrelationship between visual saliency and JND threshold. In the experimental part, we verify it from both subjective and objective aspects. In addition, the experimental results show that extracting the saliency map of the image in the discrete wavelet transform (DWT) domain and then modulating its JND threshold is better than the non-modulated JND effect.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visual Saliency Based Just Noticeable Difference Estimation in DWT Domain

et al. 2018

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“…Image watermarking is composed of two steps: i) insertion of the mark in an image; ii) extraction of the mark [4,8]. In psychovisual studies, a level of distortion that can just be seen in experimental tests is called JND (Just Noticeable Difference) [4,9]. The JND is used to adjust the embedding strength, which is optimized and adapted with respect to areas in the image.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation

Amar¹,

Harba²,

Douzi³

et al. 2017

IJCA

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Perceptual image watermarking consists in using knowledge of the Human Visual System (HVS) to choose the strength of the watermark according to image properties. This paper proposes a new perceptual image watermarking method that combines the advantages of both the wavelet domain and the spatial domain since a mixed-scale wavelet representation is applied. By considering the density of the dominant wavelet coefficients, our scheme is able to differentiate uniform, edge and texture areas. This allowed us to apply adapted luminance, edge or texture masking more efficiently. We selected effective Just Noticeable Difference models from the literature, i.e. luminance and edge masking developed by Chou and Li, and texture masking developed by Qi et al. We also took into account the HVS sensitivity, which varies with the orientation of the image activity. The method was tested on a large database of 50 color images, and compared with four other watermarking methods from the literature. Visual quality tests were conducted and the robustness to attacks was tested compared with four other watermarking methods from the literature. Results show that the proposed method yields a high visual quality and our method is very robust against attacks. This last point is of great importance for real applications. The proposed method is the best trade-off between visual quality and resistance to attacks among the tested methods.

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Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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International audienceThis chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models. There are numerous applications and we try here to remain as exhaustive as possible to provide a picture of all the applications of saliency models, but also to detect where future developments might be of interest. The applications are grouped into three categories. The first one uses the detection of the most important regions in an image and contains applications such as video surveillance, audio surveillance, defect detection, pathology detection, expressive and social gestures, computer graphics and quality metrics. The second category uses saliency maps to detect the regions which are the less interesting in an image. Here one can found applications like texture metrics, compression, retargeting, summarization, watermarking and attention-based ad insertion. Finally, a third category uses the most interesting areas in an image with further processing like comparisons between those areas. In this category one can find image registration and landmarks, object recognition, action guidance in robotics or avatars, web sites optimization, images memorability, best viewpoint, symmetries and automatic focus on images

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Visual saliency’s modulatory effect on just noticeable distortion profile and its application in image watermarking

Cited by 28 publications

References 35 publications

Visual Saliency Based Just Noticeable Difference Estimation in DWT Domain

Visual Saliency Based Just Noticeable Difference Estimation in DWT Domain

Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation

Applications of Saliency Models

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