Histogram Modification Research Articles

Reversible data hiding based on pairwise embedding and optimal expansion path

Abstract As an efficient technique for high-dimensional reversible data hiding (RDH), pairwise prediction-error expansion (pairwise PEE) has achieved better performance comparing with the conventional PEE. With pairwise PEE, the correlations among prediction-errors are well utilized by modifying the generated two-dimensional prediction-error histogram (2D-PEH). However, its performance can be further improved since the histogram modification manner (i.e., the employed modification mapping) of pairwise PEE is fixed and independent of image content. To better utilize image redundancy, instead of embedding data based on an empirically designed modification mapping, a content dependent pairwise embedding scheme is proposed in this paper. Based on a specific division of 2D-PEH, the expansion bins selection is formulated as an optimal path determination problem, and the histogram modification mapping is adaptively determined by taking the optimal expansion bins. To reduce the computation cost, a dynamic programming algorithm is proposed to solve the optimization problem with low computational complexity. Moreover, by combining the proposed optimal expansion path with the existing one-dimensional adaptive embedding mechanism, the embedding performance can be further enhanced. The proposed method performs well and its superiority is experimentally verified comparing with pairwise PEE and some other state-of-the-art methods.

Implementation System of Human Eye Tracking Algorithm Based on FPGA

With the high-speed development of transportation industry, highway traffic safety has become a considerable problem. Meanwhile, with the development of embedded system and hardware chip, in recent years, human eye detection eye tracking and positioning technology have been more and more widely used in man-machine interaction, security access control and visual detection.In this paper, the high parallelism of FPGA was utilized to realize an elliptical approximate real-time human eye tracking system, which was achieved by the series register structure and random sample consensus (RANSAC), thus improving the speed of image processing without using external memory. Because eye images acquired by the camera often generate a lot of noises due to uneven light and dark background, the preprocessing technologies such as color conversion, image filtering, histogram modification and image sharpening were adopted. In terms of feature extraction of images, the eye tracking algorithm in this paper adopted seven-section rectangular eye tracking characteristic method, which increased a section between the mouth and the nose on the basis of the traditional six-section method, so its recognition accuracy is much higher. It is convenient for the realization of hardware parallel system in FPGA. Finally, aiming at the accuracy and real-time performance of the design system, a more comprehensive simulation test was carried out.The human eye tracking system was verified on DE2-115 multimedia development platform, and the performance of VGA (resolution: 640×480) images of 8-bit grayscale was tested. The results showed that the detection speed of this system was about 47 frames per second under the condition that the detection rate of human face (front face, no inclination) was 93%, which reached the real-time detection level. Additionally, the accuracy of eye tracking based on FPGA system was more than 95%, and it has achieved ideal results in real-time performance and robustness.

An IPVO-based reversible data hiding scheme using floating predictors.

This work optimizes an improved high-fidelity reversible data hiding scheme of Peng et al. which is based on improved pixel-value-ordering (IPVO) and prediction-error expansion. In Peng et al.'s method, the difference between the maximum and second largest value (or, the minimum and second smallest value) of a block is defined considering the pixel locations of maximum and second the largest value (or, the pixel locations of minimum and second the smallest value). When the difference between the maximum and second largest value (or, the minimum and second smallest value) of a block is equal to 0 or 1, the block can be exploited to embed data. Otherwise, the block should be shifted or remain unchanged. However, different prediction-error used to embed information can lead to different histogram modification and different pixel shift rate, to further reduces the change in the carrier image. In this work, we list all the different prediction-error, which are used as the selection object for the embedded error when hiding information. As a prerequisite of meeting the demand of the embedding capacity, some appropriate prediction-errors are selected for embedding to reduce the number of the pixel shifted in the marked image as small as possible. An IPVO-based reversible data hiding scheme with floating predictor is also extended. Experimental results show that the proposed scheme yields a superior performance than the state-of-the-art works, under the condition of same embedding capacity, especially for relatively rough images.

Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed. Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images. In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE). Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality. In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously. In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE. The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding. The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE. The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches.

Real-time reversible data hiding based on multiple histogram modification

Prediction-error expansion (PEE) has been widely utilized in reversible data hiding (RDH) due to its advantage of high-quality marked image. Recently, a RDH method based on multiple histogram modification (MHM) has been proposed, achieving good performance at low capacity. However, its exhaustive expansion-bin-selection mechanism is time consuming, so that it can only choose a single pair of expansion bins in each histogram for embedding data. As an extension of MHM, a novel RDH scheme for high-capacity embedding is proposed in this paper. In the proposed method, multiple pairs of expansion bins are utilized in each histogram and a greedy search algorithm is designed to determine the nearly optimal expansion bins. The results of experiments demonstrate that the proposed scheme has real-time performance and high-quality marked image, and it outperforms the original MHM-based RDH method and some other state-of-the-art works.

Diagnostic flexible versus rigid bronchoscopy for the assessment of tracheomalacia in children.

This project compares the degree of tracheal collapse determined by rigid and flexible bronchoscopy in paediatric patients with tracheomalacia. A total of nine patients with tracheomalacia underwent both rigid and flexible video bronchoscopy. All patients were breathing spontaneously. Cross-sectional images of the airway were processed using the ImageJ program and analysed via colour histogram mode technique in order to delineate the luminal area. Paired t-tests (conducted using Stata software version 13.0) quantified differences between rigid and flexible bronchoscopes regarding the ratios of luminal pixels at maximum airway collapse to expansion. Correlation between both techniques in terms of airway collapse to expansion ratios was determined by calculating the Pearson correlation coefficient (R). The difference in ratios of maximum collapse to expansion between rigid and flexible bronchoscopy was not statistically significant (p = 0.4656) and was positively correlated (R = 0.523). The ratios suggest that rigid and flexible bronchoscopy are equally efficacious in assessing tracheomalacia severity, and may be used interchangeably in a clinical setting.

Reversible Data Hiding Using Prior Pixel Pairs in Two-Dimensional Histogram with Tri-Directional Modification of Difference Histogram

Histogram shifting and modification brought a new trend in contemporary world especially in the field of reversible data hiding (RDH) because of its rich applications. A new approach of RDH with histogram modification is introduced in the proposed work: tri-directional modification of difference histogram (TDMDH). Compared with previous proposed methods in RDH, the TDMDH method gives better embedding capacity (EC) and PSNR experimentally. Li's method proposed a difference pixel pair mapping, which embeds the data in four different directions (i.e. up, down, left, right). The proposed method enhance the work of Li's method with one more different direction embed data (i.e. diagonally up and down). This modification gives a better PSNR because of diverting more number of prior pixel pairs (PPPs) participating in embedding and fewer for shifting. This modification will gradually increase the EC with the persistence of signal-to-noise ratio. TDMDH alters two pixels to embed a bit. This modification leads to low PSNR value especially in low texture images, to improve it difference expansion is used for double-bit embedding on diagonally expanded or shifted pixel pairs. The information about embed data will be embedded to watermarked image for blind decoding. Gradient adjusted prediction and noiselevel were used to assess the PPPs, which are suitable for embedding or shifting. The proposed method secures better PSNR value (more than 50dB) from USC-SIPI image database when compared with existing methods of RDH.

Two-dimensional histogram modification based reversible data hiding using motion vector for H.264

Histogram modification (HM) is an efficient algorithm for reversible data hiding into H.264 video, motion vector based methods have been applied to HM. In this paper, we present a special strategy of reversible data hiding using motion vector based on two-dimensional histogram modification. Firstly, the two components of a motion vector will compose an embedding pair. The values of embedding pairs are classified into 17 non-intersect sets. Then, according to the set that the embedding pair belongs to, we can embed data into motion vectors by modifying the value of embedding pair. Experimental results verify that the embedding capacity is increased by two times compared with the previous algorithm, and the PSNR declines about 5%. The reversibility of the algorithm is also studied, three rules are presented to judge the reversibility of an algorithm directly, and the rules are suitable for other histogram modification based methods.

Neural Networks Based Sorting Order for Reversible Data Hiding in Pixel Prediction Error

This paper presents a Reversible Data Hiding method in grayscale digital images using Pixel Prediction Error. The prediction error of the prediction error is modified to store the secret data. The prediction of the image pixels is carried out using interpolation from neighboring pixels. The high spatial correlation of image pixels in natural images lead to prediction errors close to zero. In the second step a further interpolation of prediction errors are applied to get the prediction error of prediction errors. These errors are then modified by using histogram modification procedure to carry the secret data in binary form. A novel artificial neural networks based system is trained to find the optimal sorting order of the prediction errors for embedding. In the existing work, a simple local complexity measure is used as a proxy for pixel prediction errors. However, in the proposed work the neural networks based solution gives a more optimal order to embed pixels. Experimental results and analysis are carried out with a set of eight test images with varying characteristics. It is shown that the proposed pixel sorting method gives better visual performance for the same embedding rate compared against existing procedure. The average Peak Signal to Noise Ratio of the proposed work is 56.1 dB which is better than 54.40 dB given by existing work.

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

To protect the security of vector maps, we propose a novel reversible watermarking scheme for vector maps based on a multilevel histogram modification. First, a difference histogram is constructed using the correlations of adjacent coordinates, and the histogram is divided into continuous regions and discontinuous regions by combining the characteristics of vector map data. Second, the histogram bins that require modification are determined in the continuous regions through the optimal peak value, and the peak values are chosen from the flanking discontinuous regions in both directions; the watermarks are embedded by adopting the multilevel histogram modification strategy. The watermark extraction process is the reverse of the embedding process, and after completing the watermark extraction, the carrier data can be recovered losslessly. The experimental results show that the proposed algorithm has good invisibility and is completely reversible. Compared with similar algorithms reported previously, it achieves higher watermark embedding capacity under the same embedding distortion with lower complexity, thereby having a higher application value.

Fusion of RGB and HSV colour space for foggy image quality enhancement

The physical properties of water cause light-prompted degradation of foggy images. The light quickly loses intensity as it goes in the water, depending upon the shading range wavelength. Visible light is consumed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Foggy images with low contrast, are captured due to the degradation effects of the light spectrum. Therefore, valuable information from these images cannot be fully extracted for further processing. In this paper, the authors have proposed a new method to increase contrast and reduce the noise of foggy images using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm. The proposed method fuses the modification of image histogram into two main colour models, namely, Red–Green–Blue (RGB) and Hue-Saturation-Value (HSV). In the primary stage, the CLAHE is connected just on the red part as in water, red shading is more influenced than the blue or green shading. Furthermore, in the second stage, without influencing the hue, the CLAHE is connected to saturation and value components of the HSV colour model. Finally, enhanced image has been produced using a fusion of output of primary phase and output produced in the second phase. Two parameters, namely, RMSE (Root Mean Squared Error) and the PSNR (Peak Signal to Noise Ratio) have been considered in comparing the experimental results of the proposed system with state-of-the-art work.

Multilevel Reversible Video Watermarking with Improvement on Occluded Areas

This paper investigates the influence of the occlusion in reversible video watermarking technique based on histogram modification of motion compensated prediction error and presents a novel reversible video watermarking algorithm which has better embedding capacity and PSNR in occluded areas. In occluded areas, conventional motion estimation techniques calculate higher prediction error. Therefore, watermarking algorithm which exploits prediction error has small embedding capacity and poor image quality. To reduce the prediction error, occlusion detection algorithms must be employed in motion estimation.

Comparative analysis of integer wavelet transforms in reversible data hiding using threshold based histogram modification

In the secured data communication scenario, image based Reversible Data Hiding (RDH) at transform domain is receiving its due popularity now. In research works so far, evaluation of transforms in RDH is very minor compared to that of image coding. Since both the areas deal primarily with integers, the present study takes an appreciable step forward by investigating the utilization of integer-to-integer wavelet transforms that have reversibility, which helped in developing high capacity and robust RDH algorithms. The proposed investigation tends to bridge gaps in the area of data hiding with the available coding transforms. In this work, integer wavelet transforms like Haar, 5/3, 2/6, 9/7-M, 2/10, 5/11-C, 5/11-A, 6/14, 13/7-T, 13/7-C and 9/7-F are evaluated using a generalized threshold-based histogram shifting technique. This work compares the performance of all integer wavelet transforms and other state of the art techniques with respect to their embedding capacity and image visual quality. This analysis leads to a better understanding of the relationship between the embedding capacity and the stego image quality whenever different wavelets were utilized.

Quality characteristics prediction of complex mechatronics system based on symbolic dynamics and gray model

This paper provides a symbolic quality characteristics prediction method of complex mechatronics system based on gray model. The time series data that are collected from the design, manufacture and service process are transformed to corresponding symbolic sequence data by using the symbolic modeling analysis method. The Shannon entropy that represents the variation of symbolic quality characteristics can then be calculated, and the histogram and main modes of symbolic sequences are obtained. A preliminary prediction of system quality characteristics is conducted based on the gotten main modes. By using the fitting capability of gray theory for the trend of data sequences, the GM(1,1) model is employed for the prediction modeling of symbolic quality sequences in this paper, and the prediction of corresponding quality characteristics of whole complex mechatronics system are implemented. Furthermore, the correlation of multiple quality characteristics is analyzed by relative entropy and gray correlation analysis method. The key quality fluctuation prediction of electric control and drive system of heavy machine tool by this method has shown that the presented method has high computational efficiency and large practical value.

Progressive Dual-Domain Filter for Enhancing and Denoising Optical Remote-Sensing Images

Enhancement and denoising have always been a pair of conflicting problems in image processing of computer vision. Inspired by an earlier dual-domain filter (DDF), this letter proposes a progressive DDF to simultaneously enhance and denoise low-quality optical remote-sensing images. The main procedure of the proposed enhancement filter has two parts. First, a bilateral filter is exploited as a guide filter to obtain high-contrast images, which are enhanced by a histogram modification method. Then, low-contrast useful structures are restored by a short-time Fourier transform and are enhanced using an adaptive correction parameter. Both the quantitative and qualitative results of experiments on synthetic and real-world low-quality remote-sensing images demonstrate that the proposed method performs well on contrast enhancement, structure preservation, and noise reduction. Moreover, its satisfactory computation time resulting from its simple implementation makes it suitable for extensive application.

A New Image-Based Hybrid Reversible Data Hiding Model Using IHWT and RP-PEHM for Secured Data Communication

It is essential to maintain a proper balance between the embedding capacity, imperceptibility and robustness in image-based reversible data hiding (RDH) applications. Most of the spatial-domain RDH techniques focus more on embedding capacity and imperceptibility as compared to robustness, whereas transform-domain techniques concentrate mainly on imperceptibility and robustness. Hence, there is a need to implement a technique which maintains a proper trade-off among all the three parameters. A scheme that uses a combination of spatial- and transform-domain (hybrid) techniques is proposed here. A threshold-based difference histogram modification (TDHM) is used as a building block for data embedding and extraction. In the proposed technique, the cover image is systematically modified using spatial-domain rhombus prediction-based prediction error histogram modification and integer Haar wavelet transform (IHWT)-based TDHM (IHWT-TDHM) techniques. The experimental results show that the stated methodology accomplishes a higher embedding capacity while maintaining good imperceptibility and robustness compared to that of the direct spatial or transform RDH techniques.

A robust reversible image watermarking scheme in DCT domain using Arnold scrambling and histogram modification

Among the various watermarking schemes, the reversible watermarking scheme has drawn extensive attention in the recent years for its application in sensitive issues like medical, military and typical law-enforcement images. Cover image dependent embedding capacity and lack of robustness are the most crucial concerns of the reversible watermarking methods. To overcome these issues, a discrete cosine transform (DCT) and histogram shifting based robust reversible image watermarking scheme using Arnold scrambling is presented in this paper. Initially, the image is decomposed into non-overlapping blocks and consequently DCT are applied to each block to embed a binary bit of watermark into each transformed block by modifying one pair of middle significant AC coefficients. In this initial step, location map is also generated for the cover image restoration purpose in the extracting side. Then, this location map is embedded in the cover image using histogram modification technique. In the receiver side, at first location map is generated from an image using histogram modification method and watermark is recovered from the corresponding image. Using location map reversible image is reversed in the following extracting phase of the proposed method. The proposed reversible watermarking scheme has also been experimented to verify the robustness property against several image processing attacks and satisfactory results are achieved.

A robust reversible image watermarking scheme in DCT domain using Arnold scrambling and histogram modification

Among the various watermarking schemes, the reversible watermarking scheme has drawn extensive attention in the recent years for its application in sensitive issues like medical, military and typical law-enforcement images. Cover image dependent embedding capacity and lack of robustness are the most crucial concerns of the reversible watermarking methods. To overcome these issues, a discrete cosine transform (DCT) and histogram shifting based robust reversible image watermarking scheme using Arnold scrambling is presented in this paper. Initially, the image is decomposed into non-overlapping blocks and consequently DCT are applied to each block to embed a binary bit of watermark into each transformed block by modifying one pair of middle significant AC coefficients. In this initial step, location map is also generated for the cover image restoration purpose in the extracting side. Then, this location map is embedded in the cover image using histogram modification technique. In the receiver side, at first location map is generated from an image using histogram modification method and watermark is recovered from the corresponding image. Using location map reversible image is reversed in the following extracting phase of the proposed method. The proposed reversible watermarking scheme has also been experimented to verify the robustness property against several image processing attacks and satisfactory results are achieved.

Separable Reversible Data Hiding in Encrypted Images Based on Two-Dimensional Histogram Modification

An efficient method of completely separable reversible data hiding in encrypted images is proposed. The cover image is first partitioned into nonoverlapping blocks and specific encryption is applied to obtain the encrypted image. Then, image difference in the encrypted domain can be calculated based on the homomorphic property of the cryptosystem. The data hider, who does not know the original image content, may reversibly embed secret data into image difference based on two-dimensional difference histogram modification. Data extraction is completely separable from image decryption; that is, data extraction can be done either in the encrypted domain or in the decrypted domain, so that it can be applied to different application scenarios. In addition, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.

Laboratory Approbation of a New Approach for Contrast Enhancement of Human Face Thermal Image Based on Selective Multifunction Pixel Brightness Conversion Function

The paper suggests a new approach to improving the contrast of the thermal image of a person’s face in the deep infrared region. The approach involves the analysis and modification of the image histogram and is based on the use of a selective multivalued function for encoding the brightness of image pixels. The approach is focused on processing first of all thermal images of the person face, containing areas with various informativeness. The implementation of the approach in practice makes it possible to improve the contrast of the face image with preservation of all informative features regardless of the level of brightness of the corresponding pixels of the image.