Histogram Modification Research Articles

Adaptive feature selection and optimized multiple histogram construction for reversible data hiding

Reversible data hiding (RDH) algorithms have been extensively employed in the field of copyright protection and information dissemination. Among various RDH algorithms, the multiple histogram modification (MHM) algorithm has attracted significant attention because of its capability to generate high-quality marked images. In previous MHM methods, the prediction error histograms were mostly generated in a fixed way. Recently, clustering algorithms automatically classify prediction errors into multiple classes, which enhances the similarity among prediction errors within the same class. However, the design of features and the determination of clustering numbers are crucial in clustering algorithms. Traditional algorithms utilize the same features and fix the number of clusters (e.g., empirically generate 16 classes), which may limit the performance due to the lack of adaptivity. To address these limitations, an adaptive initial feature selection scheme and a clustering number optimization scheme based on the Fuzzy C-Means (FCM) clustering algorithm are proposed in this paper. The superiority of the proposed scheme over other state-of-the-art schemes is verified by experimental results.

A Survey on Reversible Data Hiding for Uncompressed Images

Reversible data hiding (RDH) has developed various theories and algorithms since the early 1990s. The existing works involve a large amount of specialized knowledge, making it difficult for researchers, especially primary learners, to have a good grounding in the basic ideas. In this survey, we will review the mainstream RDH algorithms in uncompressed images and analyze their unique features to provide readers with an introduction to basic topics in RDH. We analyze the most effective RDH frameworks and their common extensions. The classic techniques, including lossless compression-based RDH, difference expansion, integer transform, histogram shifting, prediction-error expansion (PEE), and their extensions, will be reviewed first. Then, three currently popular investigated schemes, i.e., multiple histograms modification, pairwise PEE, and pixel-value-ordering, are presented in detail. Four aspects of these mainstream techniques are reviewed and analyzed, including the evolution of embedding frameworks, detailed technological features, extensions, and the current state of the art. Furthermore, we look forward the possible future research based on early-age motivations.

Image enhancement based on optimized 2D histogram modification by krill herd algorithm

Image enhancement based on optimized 2D histogram modification by krill herd algorithm

Nonparametric estimation of the number of classes with different average brightness in thermal images

When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.

An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

AbstractBrain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast‐enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors’ approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation.

Correlation between Multiparametric MR Imaging and Molecular Genetics in Pontine Pediatric High-Grade Glioma.

Molecular profiling is a crucial feature in the "integrated diagnosis" of CNS tumors. We aimed to determine whether radiomics could distinguish molecular types of pontine pediatric high-grade gliomas that have similar/overlapping phenotypes on conventional anatomic MR images. Baseline MR images from children with pontine pediatric high-grade gliomas were analyzed. Retrospective imaging studies included standard precontrast and postcontrast sequences and DTI. Imaging analyses included median, mean, mode, skewness, and kurtosis of the ADC histogram of the tumor volume based on T2 FLAIR and enhancement at baseline. Histone H3 mutations were identified through immunohistochemistry and/or Sanger or next-generation DNA sequencing. The log-rank test identified imaging factors prognostic of survival from the time of diagnosis. Wilcoxon rank-sum and Fisher exact tests compared imaging predictors among groups. Eighty-three patients had pretreatment MR imaging and evaluable tissue sampling. The median age was 6 years (range, 0.7-17 years); 50 tumors had a K27M mutation in H3-3A, and 11, in H3C2/3. Seven tumors had histone H3 K27 alteration, but the specific gene was unknown. Fifteen were H3 wild-type. Overall survival was significantly higher in H3C2/3- compared with H3-3A-mutant tumors (P = .003) and in wild-type tumors compared with any histone mutation (P = .001). Lower overall survival was observed in patients with enhancing tumors (P = .02) compared with those without enhancement. H3C2/3-mutant tumors showed higher mean, median, and mode ADC_total values (P < .001) and ADC_enhancement (P < .004), with lower ADC_total skewness and kurtosis (P < .003) relative to H3-3A-mutant tumors. ADC histogram parameters are correlated with histone H3 mutation status in pontine pediatric high-grade glioma.

Reversible data hiding in encrypted images using histogram modification and MSBs integration

Reversible data hiding in encrypted images using histogram modification and MSBs integration

A new data hiding model based on adaptive keyed Huffman multi-layer midpoint folding and optimized deep wavelet histogram modification strategy

A new data hiding model based on adaptive keyed Huffman multi-layer midpoint folding and optimized deep wavelet histogram modification strategy

A Novel Reversible Data Hiding Scheme Based on Pixel-Residual Histogram

Prediction-error expansion (PEE) is the most popular reversible data hiding (RDH) technique due to its efficient capacity-distortion tradeoff. With the generated prediction-error histogram (PEH) and adaptively selected expansion bins, the image redundancy is well exploited by PEE. However, for the most widely used rhombus predictor, the rounding operation which groups different prediction-errors into one value is completely unnecessary. The embedding can be extended to a general case by removing the rounding operation, and more histogram bins can be derived for expansion with a new mapping mechanism. Therefore, in this article, instead of pixel prediction-error, we propose to compute the pixel residuals without the rounding operation, and a new embedding mechanism based on pixel-residual histogram (PRH) modification is devised. In PRH, four bins correspond to one bin in PEH. Then, different from the one-to-one mapping between the prediction-error and pixel modification, a four-to-one mapping between the pixel-residual and pixel modification is established, and the performance is optimized by adaptively selecting four expansion bin pairs for embedding. Since more modification selections are considered, better performance can be obtained. Moreover, the proposed scheme is extended to the two-dimensional (2D) histogram and multiple histograms based embedding, and the performance is further enhanced. The superiority of the proposed method is experimentally verified by comparing it with some state-of-the-art works.

Reversible Data Hiding for JPEG Images With Adaptive Multiple Two-Dimensional Histogram and Mapping Generation

Reversible data hiding based on joint photographic experts group (JPEG) images has been extensively studied to enhance embedding performance in terms of visual quality and file size preservation at the desired payload. In this paper, an efficient adaptive RDH method for JPEG images with multiple two-dimensional (2D) histogram modification is proposed. Firstly, the proposed method proposes the block smoothness estimator and the band smoothness estimator, and then combines the two estimators to reduce the embedding distortion as much as possible at the desired payload. Instead of adopting a fixed 2D mapping or choosing one from several empirically-designed mappings for each 2D histogram, the proposed method designs an adaptive 2D mapping generation strategy to adaptively generate a large number of mappings with considering the local characteristics of histogram distribution. Since exhaustively searching for the optimal mapping achieving the highest embedding performance for each 2D histogram is time-consuming, an improved discrete particle swarm optimization is utilized in the proposed method to speed up the optimization process. Extensive experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality and file size increment of the stego image.

Lateral variations in the Hosen 8‐2 vein in the Hishikari deposit, Japan: Implications for high Au‐grade zone

AbstractLateral sampling of each blasting interval (~ 2.6 m) along a 79 m strike length was conducted for the Hosen 8‐2 vein on the −5 ML (mining level, meters relative to sea level) of the Main ore zone in the Hishikari epithermal Au deposit, Kagoshima, Japan. The horizontal variation of mineral textures, bulk chemical compositions, and fluid inclusion temperatures and apparent salinity of ore fluids were determined. The major gangue minerals of the quartz veins studied include quartz and adularia with minor amounts of calcite and smectite. The major ore minerals include electrum, galena, and sphalerite with minor pyrite, petzite, and hessite. Electrum commonly occurs in a discrete band with microcrystalline quartz, granular adularia, and smectite. The electrum‐bearing band is followed by tabular adularia, and finally by granular or comb quartz. Mineral textures that indicate high degrees of supersaturation with respect to amorphous silica (presently quartz with microcrystalline/mosaic and feathery textures) and adularia (with tabular, rhombic, and granular textures) were common throughout the vein strike. Bulk chemical analyses indicate that Au grade is positively correlated with Ag, Bi, Pb, and Te contents. A bonanza zone with Au grades up to 10,800 ppm occurs in the central part of the Hosen 8‐2 vein on the −5 ML, and is associated with high adularia content, calculated as adularia / (quartz + adularia). The wide lateral variation in the geochemical composition contrasts with the consistent association of electrum with microcrystalline quartz, granular adularia, and smectite throughout the Hosen 8‐2 vein on the −5 ML. Fluid inclusion microthermometry of primary and pseudosecondary inclusions in quartz and adularia yielded histogram modes of homogenization temperature between 160 and 240°C; the most frequent mode is 200–210°C, with most data within ±10°C from this value. The maximum ice‐melting temperature of most samples excluding late comb quartz is −1.5°C, which is equivalent to an apparent salinity of 2.6 wt% NaCl eq. The wide variation in ice‐melting temperatures and apparent salinities (up to 5.2 wt% NaCl eq.) may be due to dissolved CO2 in the fluids, while the sharp decrease in apparent salinity with temperature decrease indicates CO2 loss due to fluid boiling during vein formation. The intimate association of electrum with microcrystalline quartz that recrystallized from amorphous silica indicate sharp boiling and vapor loss as the primary mechanism of Au deposition in the Hosen 8‐2 vein.

Adaptive smoothness evaluation and multiple asymmetric histogram modification for reversible data hiding

In this paper, an adaptive reversible data hiding (RDH) algorithm based on multiple asymmetric histograms is proposed by making full use of the image content. Different from existing multiple prediction error histogram (PEHs) modification methods that directly cluster all the pixels of a cover image into multiple categories, we firstly utilize a smoothness threshold to exclude as many pixels in complex regions as possible for reducing unnecessary pixel shifting, and then exploit fuzzy C-means with multiple deliberately-designed features to construct multiple sharply-distributed categories, which helps in increasing the subsequent embedding performance. Two asymmetric PEHs for each class are generated using a pair of asymmetric predictors, and the short part of each asymmetric PEH is modified to reduce the number of invalid modifications. The improved discrete particle swarm optimization is used to adaptively select the best bin while reducing computational complexity. The experimental results show that the proposed method outperforms several state-of-the-art RDH methods.

Method for spectrum holes detection based on mode analysis of spectral samples histogram

The subject of this article is the process of detection and estimation frequency boundaries of spectrum holes under conditions of high spectrum occupancy when using receivers with narrow instantaneous bandwidths. The work increases the probability of spectrum holes correct detection in conditions of high occupancy of the radio frequency spectrum and variable noise levels by developing a method to distinguish signal and noise samples based on the analysis of the histogram of spectral sample modes. The tasks to be solved are: development of a method for separation signal and noise samples in the frequency domain; development of a methodology to find the minimum mode of a multimodal probability distribution; determination of frequency boundaries of spectrum holes; formulation of recommendations for the practical implementation of developed method. The methods used are: methods of probability theory and mathematical statistics, methods of statistical modeling. The essence of the proposed method is to distinguish the set of energy spectrum samples using a threshold, obtained for the value of the histogram mode, which corresponds to noise, and to determine the frequency boundaries of spectrum holes. The following results were obtained: an expression for calculating the threshold value for separation signal and noise samples in the frequency domain using the value, which corresponds to the noise mode of the frequency samples of the probability density function. It was found that the noise mode has the smallest value among other modes, since noise samples have a smaller value compared to the signal ones. A technique for estimating the value of the noise mode has been developed, which consists of a histogram of energy spectrum frequency samples and finding the partition interval that corresponds to the value of the minimal mode. An approach was proposed to determine the frequency boundaries of noise samples in the presence of one signal in the analyzed band. Conclusions. The developed method allows detecting spectrum holes with a probability of at least 0.9 at signal-to-noise ratio values of at least 5 dB for the spectrum with a rectangular shape envelope and 12 dB for other envelopes under occupancy of up to 80%.

Trace Concealment Histogram-Shifting-Based Reversible Data Hiding with Improved Skipping Embedding and High-Precision Edge Predictor (ChinaMFS 2022)

Reversible data hiding (RDH) is a special class of steganography, in which the cover image can be perfectly recovered upon the extraction of the secret data. However, most image-based RDH schemes focus on improving capacity–distortion performance. In this paper, we propose a novel RDH scheme which not only effectively conceals the traces left by HS but also improves capacity–distortion performance. First, high-precision edge predictor LS-ET (Least Square predictor with Edge Type) is proposed, and the predictor divides pixels into five types, i.e., weak edge, horizontal edge, vertical edge, positive diagonal edge, and negative diagonal edge. Different types of target pixels utilize different training pixels with stronger local consistency to improve accuracy. Then, a novel prediction-based histogram-shifting (HS) framework is designed to conceal embedding traces in the stego images. Finally, we improve both the data-coding method and the skipping embedding strategy to improve the image quality. Experimental results demonstrate that the capacity–distortion performance of the proposed scheme outperforms the other trace concealment schemes and is comparable to the state-of-the-art schemes utilizing sorting technique, multiple histogram modification, and excellent LS-based predictors. Moreover, it can conceal the embedding traces left by the traditional HS schemes to a certain extent, reducing the risk of being steganalyzed.

Reversible Data Hiding With Brightness Preserving Contrast Enhancement by Two-Dimensional Histogram Modification

Recently, contrast enhancement with reversible data hiding (CE-RDH) has been proposed for digital images to hide useful data into contrast-enhanced images. In existing schemes, one-dimensional (1D) or two-dimensional (2D) histogram is equalized during the process of CE-RDH so that an original image can be exactly recovered from its contrast-enhanced version. However, noticeable brightness change and color distortion may be introduced by applying these schemes, especially in the case of over enhancement. To preserve image quality, this paper presents a new 2D histogram based CE-RDH scheme by taking brightness preservation (BP) into account. In particular, the row or column of histogram bins with the maximum total height are chosen to be expanded at each time of histogram modification, while the row or column of bins to be expanded next are adaptively chosen according to the change of image brightness. Experimental results on three color image sets demonstrate efficacy and reversibility of the proposed scheme. Compared with the schemes using 1D histogram, image brightness can be preserved more finely by modifying the generated 2D histogram. Moreover, our proposed scheme preserves image color and brightness while achieving better image quality than the existing schemes.

Histogram modification based pansharpening by using differential evolution algorithm

SUMMARYOptic geo‐observer satellites acquire multispectral images (MSI) with low spatial resolution and panchromatic images (PAN) with high spatial resolution, due to communication band‐limits and the necessity of using on‐board energy efficiently. Histogram modification is an image processing method that alters the intensity values of an image to improve contrast. The Histogram Modification based Pansharpening (HMP) method has been proposed in this article for injecting high‐resolution spatial information of PAN into MSI with limited color degeneration to generate a pansharpened image (PSI). The differential evolution algorithm (DE) has been used to optimize the internal parameters of the HMP. HMP uses three different blind‐image quality metrics to avoid from artificial spectral and spatial defects generally appeared in PSI. The quality of PSI generated by HMP has been statistically compared to the PSIs produced by 17 different pansharpening methods by using 10 different image quality metrics. In the tests performed, three different test image sets acquired by geo‐observation satellites have been used. Test image sets are covering the areas with different spatial and spectral characteristics. HMP outperforms the other state‐of‐the‐art pansharpening methods used in this article, according to the statistical analysis results. HMP is highly effective at reliably preserving spectral and spatial fidelity.

Correction to: Reversible data hiding in H.264/AVC videos based on hybrid-dimensional histogram modification

Correction to: Reversible data hiding in H.264/AVC videos based on hybrid-dimensional histogram modification

Grayscale-Invariant Reversible Data Hiding Based on Multiple Histograms Modification

Grayscale-invariant reversible data hiding (GI-RDH) in color images is a data embedding framework in which the grayscales of a marked color image must be identical to those of the host color image. Recently, some state-of-the-art GI-RDH schemes were proposed. However, their performance in embedding distortion is unsatisfactory. In order to obtain better image quality, a well-known histogram-shifting-based RDH method called multiple histograms modification (MHM) is considered. In this paper, we propose an MHM-based GI-RDH scheme. First, we modified our previous GI-RDH scheme using a multiple-histogram-shifting approach instead of a difference expansion approach. Next, we designed a procedure to select expansion–bin pairs for generated histograms to achieve low embedding distortion through further data embedding. Specifically, we analyzed the expected embedding distortion of our MHM-based GI-RDH scheme given any set of expansion–bin pairs. We then formulated an optimization problem called the GI-MHM minimization problem to identify the optimal expansion–bin pairs for further embedding tasks. Finally, we generated an approximated solution for the GI-MHM minimization problem and conducted the embedding task with these selected expansion–bin pairs. The experimental results revealed that the proposed GI-RDH scheme outperformed previous methods when the embedding capacity was small.

General Expansion-Shifting Model for Reversible Data Hiding: Theoretical Investigation and Practical Algorithm Design

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance.

Multiple Histograms-Based Reversible Data Hiding Using Fast Performance Optimization and Adaptive Pixel Distribution

Abstract In prediction error-based reversible data hiding, multiple histograms modification (MHM) is well known for high image quality and thus has received wide attention in recent years. However, the computational cost for performance optimization in MHM is too high, which is particularly critical for real-time applications. This manuscript aims to reduce the computational complexity of MHM by presenting two techniques, including fast performance optimization and adaptive pixel distribution. Fast performance optimization provides a two-stage process for optimal bin selection by exploiting the concept of per-bit distortion of data embedding within a prediction error histogram (PEH). In fast performance optimization, the distribution characteristics of the per-bit distortion are investigated to significantly narrow down the solution space of optimal bin selection. The second technique is adaptive pixel distribution, which tries to nonuniformly allocate pixels into multiple PEHs to further reduce the time complexity. Extensive experiments show that the computational complexity of MHM is significantly reduced while well preserving the image quality.