Texture Masking Research Articles

A Hybrid Contrast and Texture Masking Model to Boost High Efficiency Video Coding Perceptual Rate-Distortion Performance

A Hybrid Contrast and Texture Masking Model to Boost High Efficiency Video Coding Perceptual Rate-Distortion Performance

Image Quality Assessment by Combining Masking Texture and Perceptual Color Difference Model

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

Robust and blind image watermarking via circular embedding and bidimensional empirical mode decomposition

In this paper, a robust and blind image watermarking algorithm via circular embedding and bidimensional empirical mode decomposition (BEMD) is developed. First, the watermark image is scrambled by Arnold transform to increase the security of the algorithm. Second, the Hilbert curve is adopted to reduce the scrambled 2D watermark image to one-dimensional watermark signal. Third, the host image is decomposed by BEMD to obtain the multi-scale representation in the forms of intrinsic mode functions (IMFs) and a residue. Then, the extreme points of the IMFs are extracted as the embedding locations. Finally, the one-dimensional watermark signal is repeatedly and cyclically embedded in the extreme locations of the first IMF according to the texture masking characteristics of the human visual system, which greatly improves the ability of our algorithm against various attacks. The final watermarked image is reconstructed by combining the modified first IMF and the residual. The watermark can be successfully extracted without resorting to the original host image. Furthermore, image correction can be applied before image watermarking extraction if there are geometric attacks in watermarked image. A large number of experimental results and thorough evaluations confirm that our method can obtain higher imperceptibility and robustness under different types of attacks, and achieve better performance than the current state-of-the-art watermarking algorithms, especially in large-scale cropping attack, JPEG compression, Gaussian noise, sharpening, Gamma correction, scaling, histogram equalization, and rotation attacks.

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

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.

Visual Attention Guided Pixel-Wise Just Noticeable Difference Model

The just noticeable difference (JND) models in pixel domain are generally composed of luminance adaptation (LA) and contrast masking (CM), which takes edge masking (EM) and texture masking (TM) into consideration. However, in existing pixel-wise JND models, CM is not evaluated appropriately since they overestimate the masking effect of regular oriented texture regions and neglect the visual attention characteristic of human eyes for the real image. In this work, a novel JND model in pixel domain is proposed, where orderly texture masking (OTM) for regular texture areas (also called orderly texture regions) and disorderly texture masking (DTM) for complex texture areas (also called disorderly texture regions) are presented based on the orientation complexity. Meanwhile, the visual saliency is set as the weighting factor and is incorporated into CM evaluation to enhance JND thresholds. Experimental results indicate that compared with existing relevant JND profiles, the proposed JND model tolerates more distortion in the same perceptual quality, and brings better visual perception in the same level of the injected JND-noise energy.

Image fusion based on wavelet transform with genetic algorithms and human visual system

A novel wavelet-based approach for multi-focus image fusion is presented, which is developed by taking into not only account the characteristics of human visual system (HVS) but also consider the optimization of image quality index to meet the human perception. After the multi-focus images to be fused are decomposed by the wavelet transform, different-fusion schemes for combining the coefficients are proposed: coefficients in low-frequency band are using the genetic algorithms to estimate the optimal weight according to the Edge-Association Index, and coefficients in high-frequency bands are weighted fusion by the texture masking of human visual system. To overcome the presence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performed by a window-based consistency verification process. The fused image is finally constructed by the inverse wavelet transform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing fusion methods are carried out in the paper. Experimental results on simulated and real images indicate that the proposed method is effective and can get satisfactory fusion results.

Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

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.

A Just Noticeable Difference-Based Video Quality Assessment Method with Low Computational Complexity

A Just Noticeable Difference (JND)-based video quality assessment (VQA) method is proposed. This method, termed as JVQ, applies JND concept to structural similarity (SSIM) index to measure the spatial quality. JVQ incorporates three features, i.e. luminance adaptation, contrast masking, and texture masking. In JVQ, the concept of JND is refined and more features are considered. For the spatial part, minor distortions in the distorted frames are ignored and considered imperceptible. For the temporal part, SSIM index is simplified and used to measure the temporal video quality. Then, a similar JND concept which comprises of temporal masking is also applied in the temporal quality evaluation. Pixels with large variation over time are considered as not distorted because the distortions in these pixels are hardly perceivable. The final JVQ index is the arithmetic mean of both spatial and temporal quality indices. JVQ is found to achieve good correlation with subjective scores. In addition, this method has low computational cost as compared to existing state-of-the-art metrics.

Estimating just‐noticeable distortion for images/videos in pixel domain

Existing pixel-based just noticeable distortion (JND) models only take into account luminance adaptation and texture masking (TM). Similarly, existing discrete cosine transform (DCT) based models do not take into account foveal vision effects and do not estimate TM efficiently. As human visual system (HVS) is not sensitive to distortion below the JND threshold, estimation of the perceptual visibility threshold is widely used in digital and video processing applications. The authors propose a comprehensive and efficient pixel-based JND model incorporating all major factors which contribute to the JND estimation. The evaluation of contrast masking (CM) is done by distinguishing the edge and TM with respect to the entropy masking properties of the HVS. Similarly, the foveal vision effects are also taken into account for the comprehensive estimation of contrast sensitivity function (CSF). Hence, the proposed pixel-based JND model incorporates the spatio-temporal CSF, foveal vision effects, influence of eye-movement, luminance adaptation and CM to be more consistent with human perception. The incorporation of these important factors makes the proposed model the most comprehensive and efficient in the current literature. Psychophysical experiments were performed to test the proposed model. The results show the proposed model comprehensively outperforms other existing models proving its efficiency.

A Perception-Based Hybrid Model for Video Quality Assessment

It is known that the human visual system (HVS) employs independent processes (distortion detection and artifact perception—also often referred to as near-threshold and suprathreshold distortion perception) to assess video quality for various distortion levels. Visual masking effects also play an important role in video distortion perception, especially within spatial and temporal textures. In this paper, a novel perception-based hybrid model for video quality assessment is presented. This simulates the HVS perception process by adaptively combining noticeable distortion and blurring artifacts using an enhanced nonlinear model. Noticeable distortion is defined by thresholding absolute differences using spatial and temporal tolerance maps that characterize texture masking effects, and this makes a significant contribution to quality assessment when the quality of the distorted video is similar to that of the original video. Characterization of blurring artifacts, estimated by computing high frequency energy variations and weighted with motion speed, is found to further improve metric performance. This is especially true for low quality cases. All stages of our model exploit the orientation selectivity and shift invariance properties of the dual-tree complex wavelet transform. This not only helps to improve the performance but also offers the potential for new low complexity in-loop application. Our approach is evaluated on both the Video Quality Experts Group (VQEG) full reference television Phase I and the Laboratory for Image and Video Engineering (LIVE) video databases. The resulting overall performance is superior to the existing metrics, exhibiting statistically better or equivalent performance with significantly lower complexity.

Efficient wavelet‐based perceptual watermark masking for robust fingerprint image watermarking

In this study, a robust wavelet-based fingerprint image watermarking scheme using an efficient just perceptual weighting (JPW) model has been proposed. The JPW model exploits three human visual system characteristics, namely: spatial frequency sensitivity, local brightness masking and texture masking, to compute a weight for each wavelet coefficient, which is then used to control the amplitude of the inserted watermark. The idea is motivated by the fact that fingerprint images perceptually differ from natural images and a JPW model adapted to such images would further enhance the robustness of the watermarking scheme. Experimental results show that the proposed model significantly improves the performance of the conventional watermarking technique in terms of robustness while maintaining the same imperceptibility of the watermark. Finally, the proposed technique has shown a clear superiority over a number of related state-of-the-art masking techniques.

Digital Watermarking Payload Estimation Based on Human Visual System

This paper proposes the estimation method for the maximum payload on spatial domain, concentrates on digital watermarking payload in the spatial domain image, on the constraint of perceptual invisibility research, the influence under the factors in Human Visual System. The maximum payload is influenced by the factors which include the size of image, the brightness masking, contrast masking and texture masking of the image. with such as noise visibility function visual model, gets the just noticeable different value to calculate the payload of the image, finally we get the watermarking payload, test and verify it with Matlab simulation experiments.

Reduced-Reference Video Quality Assessment of Compressed Video Sequences

In this paper, a novel reduced-reference (RR) video quality assessment (VQA) is proposed by exploiting the spatial information loss and the temporal statistical characteristics of the interframe histogram. From the spatial perspective, an energy variation descriptor (EVD) is proposed to measure the energy change of each individual encoded frame, which results from the quantization process. Besides depicting the energy change, EVD can further simulate the texture masking property of the human visual system (HVS). From the temporal perspective, the generalized Gaussian density (GGD) function is employed to capture the natural statistics of the interframe histogram distribution. The city-block distance (CBD) is used to calculate the histogram distance between the original video sequence and the encoded one. For simplicity, the difference image between adjacent frames is employed to characterize the temporal interframe relationship. By combining the spatial EVD together with the temporal CBD, an efficient RR VQA is developed. Evaluation on the subjective quality video database demonstrates that the proposed method outperforms the representative RR video quality metric and the full-reference VQAs, such as peak signal-to-noise ratio and structure similarity index in matching subjective ratings. This means that the proposed metric is more consistent with the HVS perception. Furthermore, as only a small number of RR features are extracted for representing the original video sequence (each frame requires only one parameter for describing EVD and three parameters for recording GGD), the RR features can be embedded into the video sequences or transmitted through the ancillary data channel, which can be used in the video quality monitoring system.

Reduced-reference image quality assessment in reorganized DCT domain

In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by depicting the subband statistical characteristics in the reorganized discrete cosine transform (RDCT) domain. First, the block-based DCT coefficients are reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For the intra RDCT subband characteristic, the coefficient distribution of each RDCT subband is modeled by the generalized Gaussian density (GGD) function. The city-block distance (CBD) is employed to measure the modeling error between the actual distribution and the fitted GGD curve. For the inter RDCT subband characteristic, the mutual information (MI) is utilized to depict the dependencies between coefficient pairs in related RDCT subbands. Moreover, a frequency ratio descriptor (FRD) calculated in the RDCT domain is proposed to depict how the image energy distributes among different frequency components. The FRD values computed from both the reference and distorted images are jointly considered to derive a novel mutual masking strategy for simulating the texture masking property of the human visual system (HVS). By considering the GGD modeling of intra RDCT subband, MI of inter RDCT subbands, and FRD of the image, the proposed RR IQA is developed. Experimental results demonstrate that a small number of RR features is sufficient to represent the reference image for the perceptual quality analysis. The proposed method can outperform the state-of-the-art RR IQAs, and even the full-reference (FR) PSNR and SSIM.

Haar‐Wavelet‐Based Just Noticeable Distortion Model for Transparent Watermark

Watermark transparency is required mainly for copyright protection. Based on the characteristics of human visual system, the just noticeable distortion (JND) can be used to verify the transparency requirement. More specifically, any watermarks whose intensities are less than the JND values of an image can be added without degrading the visual quality. It takes extensive experimentations for an appropriate JND model. Motivated by the texture masking effect and the spatial masking effect, which are key factors of JND, Chou and Li (1995) proposed the well‐known full‐band JND model for the transparent watermark applications. In this paper, we propose a novel JND model based on discrete wavelet transform. Experimental results show that the performance of the proposed JND model is comparable to that of the full‐band JND model. However, it has the advantage of saving a lot of computation time; the speed is about 6 times faster than that of the full‐band JND model.

Practical Image Quality Metric Applied to Image Coding

Perceptual image coding requires an effective image quality metric, yet most of the existing metrics are complex and can hardly guide the compression effectively. This paper proposes a practical full-reference metric with consideration of the texture masking effect and contrast sensitivity function. The metric is capable of evaluating typical image impairments in real-world applications and can achieve the comparable performance as the state-of-the-art metrics on the publicly available subjectively-rated image databases. Due to its simplicity, the metric is embedded into JPEG image coding to ensure a better perceptual rate-distortion performance.

An Image Adaptive Watermarking Algorithm Based on Ridgelet Transform and Two-Dimensional Fuzzy Partition

An image adaptive watermarking algorithm based on ridgelet transform and two- dimensional(2-D) fuzzy partition classification is proposed. In order to obtain a sparse representation of straight edge singularity, the image is first partitioned into small pieces and the ridgelet transform is applied for each piece. After analyzing texture distribution in ridgelet coefficients of each piece, two feature vectors are selected to make up for the ‘wrap around’ effect for FRIT on representation of the image texture. Then the image is classifed into frat regions and texture regions by applying 2-D fuzzy partition classification algorithm with the two feature vectors prepocessed. An watermark sequence is embedded into texture regions with the embedding strength adaptively adjusted by ridgelet coefficients based on the feature of luminance masking and texture masking. Experimental results prove robustness and transparency of the proposed watermarking scheme.

Spread Spectrum Image Watermarking Based on Perceptual Quality Metric

Efficient image watermarking calls for full exploitation of the perceptual distortion constraint. Second-order statistics of visual stimuli are regarded as critical features for perception. This paper proposes a second-order statistics (SOS)-based image quality metric, which considers the texture masking effect and the contrast sensitivity in Karhunen-Loève transform domain. Compared with the state-of-the-art metrics, the quality prediction by SOS better correlates with several subjectively rated image databases, in which the images are impaired by the typical coding and watermarking artifacts. With the explicit metric definition, spread spectrum watermarking is posed as an optimization problem: we search for a watermark to minimize the distortion of the watermarked image and to maximize the correlation between the watermark pattern and the spread spectrum carrier. The simple metric guarantees the optimal watermark a closed-form solution and a fast implementation. The experiments show that the proposed watermarking scheme can take full advantage of the distortion constraint and improve the robustness in return.

A Quality Assessment of Watermarked Color Image Based on Vision Masking

The quality assessment of the watermarked color image is one of the most important aspects that evaluate the performance of watermarking system. PSNR is the common quality assessment of the watermarked image. But the results measured by PSNR usually do not match to human vision well and result in misleading. The discrete cosine transform was used as the image analysis tools. The psychophysical research -- the luminance, texture, frequency and color masking on human vision were analyzed and used in masking strength calculation. Then a vision masking calculation method of image DCT coefficients was given in this paper. Based on the masking strength calculation, a new quality assessment of watermarked color image was proposed. Experimental results showed that the new quality assessment outperforms PSNR. The distorted image quality values calculated using the new quality assessment are consistent with the perception of human vision and have high correlation and consistency with subjective values. proposed a DCT(Discrete Cosine Transform) domain watermark assessment method based on Laplacian model. For the same watermarking algorithm, embed location and watermark sequence, the evaluation results become worse with the increase of the embed strength. The algorithm can't distinguish the difference when we changed the length of embedded sequence under the same embed strength. By using the frequency masking, Ming, Hao and Yang (10) proposed a weighted image measurement, while the luminance and texture masking were not considered. By calculating the just noticeable difference (JND) for each DCT coefficient of image with watson's JND model, Yang and Liang (11) assigned

Just Noticeable Difference for Images With Decomposition Model for Separating Edge and Textured Regions

In just noticeable difference (JND) models, evaluation of contrast masking (CM) is a crucial step. More specifically, CM due to edge masking (EM) and texture masking (TM) needs to be distinguished due to the entropy masking property of the human visual system. However, TM is not estimated accurately in the existing JND models since they fail to distinguish TM from EM. In this letter, we propose an enhanced pixel domain JND model with a new algorithm for CM estimation. In our model, total-variation based image decomposition is used to decompose an image into structural image (i.e., cartoon like, piecewise smooth regions with sharp edges) and textural image for estimation of EM and TM, respectively. Compared with the existing models, the proposed one shows its advantages brought by the better EM and TM estimation. It has been also applied to noise shaping and visual distortion gauge, and favorable results are demonstrated by experiments on different images.