JND-Guided Perceptually Color Image Watermarking in Spatial Domain

Just noticeable difference (JND) reflecting visual redundancy of human visual system (HVS) has been widely adopted in image/video processing. In this work, we introduce a novel JND model in pixel domain for screen content (SC) images. Due to the obvious different impacts on HVS between pictorial region and computer-generated texture, we decompose a SC image into screen content set (SCS) and non-screen content set (non-SCS) for JND modeling. For SCS, edge masking effect is mainly considered by using a parametric model with an adaptive edge representation. For non-SCS, both the edge masking effect and pattern masking effect are taken into account to improve distortion tolerance by injecting more noise to pattern complexity region without perceptual quality degradation. Furthermore, visual saliency is employed to adjust pattern masking effect in the view of HVS. Experimental results show that compared with state-of-the-arts, the proposed JND model can tolerate more distortion, and provide better perceptual quality at the same noise level.

With the rapid access convenience of content brought by 5G technology, the integrity protection of content becomes more important. The reversible visible watermarking algorithm has attracted more attention due to its effective content protection. In this paper, a novel improved reversible visible image watermarking scheme based on gradient-weighted class activation mapping (Grad-CAM) and the just noticeable difference (JND) model has been presented. The proposed region of interest (ROI) selection strategy is used to locate the main protected body of images for watermark embedding. Divide the watermark and ROI into nonoverlapping blocks in the same way and then embed the classified two types of watermark blocks into corresponding ROI blocks with the JND model. The optimal bit positions for watermark embedding can be selected adaptively with JND threshold and achieve the tradeoff between the watermark visibility and watermarked image quality. For lossless image recovery and watermark extraction, the recovery information is reversibly hidden into watermarked image. In the experiments, the same process of grayscale images is used to each channel separately for color images watermarking. Besides, there are six aspects in this paper to estimate the proposed scheme; with the comparison to other reversible visible watermarking schemes, experimental results demonstrate the effectiveness of our proposed scheme.

Towards perceptual image watermarking with robust texture measurement

Macrophotography Images (MPIs) have recently emerged as an active topic due to the development of mobile phone camera technology. A large number of MPIs have been rapidly increasing in many rich visual services, such as smartphones or high-definition monitors. MPIs are often composed of sharp macroimage and blur background, which exhibit different perceptual properties that often lead to different just noticeable difference (JND) estimation. Inspired by this, we formulate the blur concealment (BC) as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in the sharp regions, and is complicated with a strong masking effect in the blur parts. Furthermore, texture and orientation adaption and color information weighting are separately incorporated into the contrast masking and color masking. Finally, considering both BC and masking effects, a novel robust watermarking framework based on the proposed blur-guided JND model for MPIs, targeting at further improving the MPIs copyright protection performance. Extensive experiments on MP2020 and Blur Detection data sets show that the applicability of the proposed JND model in the scenario of perceptually MPIs watermarking, and our proposed scheme can outperform the state-of-the-art watermarking schemes by providing better robustness performance at the uniform visual quality.

Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding. Inspired by this, we analyze the effect of content complexity on visual masking, and calculate the content complexity with the distribution of orientation. Then, by considering the effect from content complexity, contrast sensitivity function, and luminance adaption, a novel JND model in Discrete Cosine Transform (DCT) domain is proposed. Experimental results demonstrate the effectiveness of the proposed model for JND estimation.

In recent years, digital image watermarking has gained a significant amount of popularity and developed into a crucial and essential tool for copyright protection, security, and the identification of multimedia content. Despite its high computational complexity, singular value decomposition (SVD) is an extensively utilized transformation in digital image watermarking. This research presents a robust and blind image watermarking scheme that directly alters the image pixels in the spatial domain to incorporate the watermark by quantizing the block-wise invariant maximum singular value. Using a distribution rule, pixels from the cover image are redistributed to obtain a new image that is divided into square and non-overlapping blocks to obtain invariant maximum singular values by using the matrix 2-norm in the spatial domain without performing an SVD transform. This modifies the pixels of the cover image such that the outcome is equivalent to the difference between the maximum singular values of the corresponding blocks in covers and watermarked images. The strengths of the proposed approach are highlighted by a comparison of experimental results with the most recent and comparable watermarking approaches.

A new pyramidal JND (just noticeable difference) model is presented. The idea is to use this JND to determine the optimum strength for embedding the watermark providing an invisible and robust watermarking scheme. The image is first decomposed into a multiresolution representation using the pyramidal decomposition. Then, a perceptual model is proposed to compute the JND value for each pixel at each Laplacian level. This model takes into account three main characteristics of the human visual system (HVS), namely: contrast sensitivity, luminance adaptation and contrast masking. The performance of the proposed technique is evaluated in terms of transparency, using subjective and objective tests, and robustness to different common attacks.

A fast Coding Unit (CU) partition decision strategy based on Human Visual System (HVS) perception quality is proposed in this paper. Considering that it is difficult for existing fast algorithms to further improve compression efficiency, perceptual coding technology has been tried to remove visual redundancy for achieving the purpose of reducing the bit rate on the basis of maintaining subjective visual quality. However, the existing perceptual coding model is still insufficient to reflect the characteristics of the HVS, which has limited improvement in coding efficiency, especially in fast algorithms. In this method, the characteristics of the limited human capacity for spatial-temporal resolution and the visual sensory memory are used to improve coding performance. First, the color complexity is used as a control factor to optimize the Just Noticeable Difference (JND) model to remove visual redundancy in a way that is more in line with the characteristics of visual perception. Second, a classification model of motion patterns based on human visual saliency is designed to provide a basis for CU classification, which effectively improves the coding accuracy. Finally, an offline Decision Tree (DT) classifier is designed based on the above model, and texture features are incorporated into the classifier as another key attribute to further reduce the computational complexity. The results of performance evaluation confirm that the proposed method achieves significantly improved coding performance compared with original Versatile Test Model (VTM). Compared with existing algorithms, our method not only improves coding efficiency, but also improves subjective visual quality.

Generally, High Boost filter is used to emphasize high frequency components representing the image details without eliminating low frequency components representing the basic form of the signal. The dissimilarity between the watermark and unwatermarked parts of the image are increased by this filter. Thus, watermark could be recovered significantly better by recovery algorithm. In this paper, a comparison is taken place between the effects of different places of preforming High Boost filter on the reliability of the recovery methods in spatial domain image watermarking. This filter is convolved in two different places in the watermark extraction algorithm; before running the watermark extraction and before calculate the correlation in the middle of extraction. This paper concentrate to conclude which of these places is more proper to convolve this filter. A classic correlation based method is used as a representative of spatial domain watermarking. Several experiment are done to show which one of this places is more proper for preforming proposed filter.

Treating color patterns in a geometric morphometrics framework reveals rapid rates of color evolution that are explained by a combination of intrinsic organismal features (color variation among patches) and geography within a cosmopolitan radiation of birds.

Quantization parameter based blind watermarking schemes are very popular. These schemes work in spatial domain as well as in various transform domains such as wavelet, discrete cosine, and real oriented wavelet. These schemes utilize one bit (binary) image as a watermark. However, in general, watermarks are any n bit image, where n ≥ 1. Therefore, these schemes are not suitable for the watermarks with n > 1. In this paper, we extend the existing work on blind watermarking schemes for n bits watermark images. The ‘division algorithm’ is used as a key component to develop the extended schemes. The proposed scheme is implemented in spatial domain. Experiments are done on one bit, two bits and eight bits watermark images. The results are investigated qualitatively by visualization, and measured quantitatively by peak-signal-to-noise-ratio (PSNR) and normalized correlation coefficient (NC). The results of the existing and proposed watermarking schemes are same for n = 1. The results of the proposed watermarking scheme are reliable for n > 1. A formula is proven for lower bound on PSNR, which is a key finding in the watermarking community. The formula is verified experimentally.

With the consideration that incorporating visual saliency information appropriately can benefit image quality assessment metrics, this paper proposes an objective stereoscopic video quality assessment (SVQA) metric by incorporating stereoscopic visual attention (SVA) to SVQA metric. Specifically, based upon the multiple visual masking characteristics of HVS, a stereoscopic just-noticeable difference model is proposed to compute the perceptual visibility for stereoscopic video. Next, a novel SVA model is proposed to extract stereoscopic visual saliency information. Then, the quality maps are calculated by the similarity of the original and distorted stereoscopic videos’ perceptual visibility. Finally, the quality score is obtained by incorporating visual saliency information to the pooling of quality maps. To evaluate the proposed SVQA metric, a subjective experiment is conducted. The experimental result shows that the proposed SVQA metric achieves better performance in comparison with the existing SVQA metrics.

Contrast enhancement which aims to increase the contrast of an image with low dynamic range, has been widely studied and exploited. In spite of the great success of many contrast enhancement algorithms, they still have difficulty in achieving both global and local contrast enhancement so that some over-enhancement, under-enhancement or even halo artifacts are often produced in complex images. This paper proposes a simple but efficient contrast enhancement method which may achieve both global and local contrast enhancement and perceptually suppress the above-mentioned problems. A cost function integrating global enhancement with local contrast enhancement is firstly constructed to pose image enhancement as an optimization problem. Then, two key steps are involved in solving for an optimal solution, the just-noticeable difference (JND) model is introduced to perceptually determine maximum local gains and neighbouring gray-level difference for local and global contrast enhancement, respectively, and an adaptive parameter regularization method is invoked to further suppress over-enhancement and halo artifacts. The experimental results on many images both qualitatively and quantitatively demonstrate our algorithm can robustly provide better visual quality in global and local contrast compared to a selection of other well-known state-of-the-art algorithms.

In modern times, the rapid growth of the Internet has made copyright protection of digital contents a critical issue. A Digital Rights Management (DRM) system is aimed at protecting the high-value digital assets and controlling the distribution and utilization of those digital assets. Watermarking technologies are being regarded as a vital mean to proffer copyright protection of digital images. Digital watermarking hides, in digital images, the information necessary for ownership identity to offer copyright protection. This paper proposes an innovative invisible and blind watermarking scheme for copyright protection of digital images with the purpose of defending against digital piracy. In the proposed watermarking scheme, a binary watermark image is invisibly embedded into the host image for achieving copyright protection. In watermark embedding, every pixel of the watermark image is embedded into the individual blocks of the host image sized 2×2. In the proposed watermarking scheme, the process of watermark extraction necessitates only the watermarked image and it doesn't require the original image or any of its characteristics, and hence, the proposed watermarking scheme is blind. The efficiency of the proposed watermarking scheme has been demonstrated via the experimental results.

To protect the ownership of the digital products, a novel adaptive and blind watermarking scheme is designed. Firstly, the underlying reason of image quality degradation in singular value decomposition (SVD)-based watermarking scheme is analyzed and the potential scenario causing visible destroy is pointed out. Then the optimal SVD blocks selection strategy is proposed to improve the imperceptibility. Different from other block selection rules devised by subjective evaluation means, our selection rule aims to retain the image quality as much as possible from the source. Furthermore, information entropy is utilized to achieve the purpose of adaptive embedding. In the experiment, the proposed watermarking scheme is tested under several attacks, such as noise attack, JPEG compression, blurring, sharping, and etc. Finally, the proposed watermarking scheme is compared with other existing schemes, and the experimental results demonstrate the robustness, imperceptibility and superior of the proposed watermarking scheme.