A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level ...differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k+l) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k+l, and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.
A necessary condition for optimal use in decision making of probability forecasts in general, and ensemble forecasts in particular, is that they should be calibrated. Ensemble calibration implies ...that each observation is statistically indistinguishable from the members of its forecast ensemble. The dimensionality of multivariate forecast settings, where the observation is a vector of related quantities rather than a scalar, affords opportunities for a rich variety of types of ensemble miscalibration, and so requires more sophisticated metrics for detection and diagnosis. This article investigates the performance in an artificial data setting of five multivariate ensemble calibration metrics that have been proposed in the literature. It is found that none of these dominates the others for detecting covariance miscalibration across a variety of miscalibration types, and that each exhibits weak performance in some instances, implying that several of the metrics should be used simultaneously when multivariate ensemble miscalibration is to be investigated. Multivariate biases were detected more strongly and diagnostically using univariate rank histograms for the individual forecast vector components.
This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern ...recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.
This paper proposes a dictionary-based histogram packing technique for lossless image compression. It is used to improve the performance of the state-of-the-art lossless image compression standards ...and methods when compressing sparse and locally sparse histogram images. The proposed method leverages inter-block correlations and similarities not only within the neighborhood but also across the entire image, thereby effectively reducing the block boundary artifacts commonly observed in block-based histogram packing techniques. To achieve this, a dictionary is employed to represent highly correlated blocks using a key that captures the union of their active symbol sets. Experimental results have demonstrated that the proposed method, when applied to sparse and locally sparse histogram images, enhances the performance of various state-of-the-art lossless image compression techniques. Notably, improvements were observed in standards and methods such as JPEG-2000, JPEG-LS, JPEG-XL, PNG, and CALIC.
•Proposed a dictionary-based histogram packing technique for lossless image compression.•Improved the performance of state-of-the-art lossless image compression standards and methods for sparse and locally sparse histogram images.•Leveraged inter-block correlations and similarities across the entire image to effectively reduce block boundary artifacts.•Utilized a dictionary to represent highly correlated blocks using a key representing their active symbol sets.•Demonstrated through experiments the enhanced performance of the proposed method in various lossless image compression techniques.
Reversible Data Hiding Techniques (RDH) play an increasingly pivotal role in the field of cybersecurity. Overlooking the properties of the carrier image and neglecting the influence of texture can ...lead to undesirable distortions and irreversible data hiding. In this paper, a novel block-based RDH technique is proposed that harnesses the relative correlation between multidirectional prediction error histograms (MPEH) and pixel fluctuation values to mitigate undesirable distortions and enable RDH, thereby ensuring heightened security and efficiency in the distribution process and improving the robustness of the block-based RDH technique. The proposed technique uses a combination of pixel fluctuation and local complexity measures to determine the best embedding locations within smooth regions based on the cumulative peak regions of the MPEH with the lowest fluctuation values. Similarly, during the extraction process, the same optimal embedding locations are identified within smooth regions. The multidirectional prediction error histograms are then used to accurately extract the hidden data from the pixels with lower fluctuation values. Overall, the experimental results highlight the effectiveness and superiority of the proposed technique in various aspects of data embedding and extraction, and demonstrate that the proposed technique outperforms other state-of-the-art RDH techniques in terms of embedding capacity, image quality, and robustness against attacks. The average Peak Signal-to-Noise Ratio (PSNR) achieved with an embedding capacity ranging from 0.5×104 bits to 5×104 bits is 52.72 dB. Additionally, there are no errors in retrieving the carrier image and secret data.
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.
•Employ FCM clustering to build multiple closely correlated carriers for MH_RDH.•Attempt to apply machine learning algorithm to reversible data hiding applications.•Effectively represent each pixel ...with a set of features, which will be used in FCM.•Optimize the hyper parameters in clustering process for better performance in MH_RDH.•Experimental results show the superiority of our FCM based MH_RDH.
Reversible data hiding algorithm (RDH) has been widely used in multimedia’s copyright protection and content integrity authentication. As a typical RDH scheme, histogram shifting (HS) is extensively investigated due to its high quality of stego-image. Most existing HS based RDH schemes utilize prediction and sorting techniques to build single sharp histogram, which exploit the smooth areas in cover image for data hiding. To take advantages of the correlation among image contents of different texture characteristics, several multiple histograms based RDHs (MH_RDH) are proposed recently, which resort on some rigid rules, e.g. single feature based sorting followed by uniform segmentation of sorted sequence, to construct the multiple histograms. In this paper, the clustering algorithm, i.e. Fuzzy C-means (FCM) clustering, is introduced for the construction of multiple histograms. The FCM equipped with deliberately designed features is employed to classify the cover carriers, e.g. prediction errors, into different clusters with similar traits, which are then used to build the multiple histograms for efficient data embedding. Experimental results demonstrate the superior performance of the proposed scheme over other state-of-the-art ones.
We introduce Parallel Histogram Plot (PHP), a technique that overcomes the innate limitations of parallel coordinates plot (PCP) by attaching stacked-bar histograms with discrete color schemes to ...PCP. The color-coded histograms enable users to see an overview of the whole data without cluttering or scalability issues. Each rectangle in the PHP histograms is color coded according to the data ranking by a selected attribute. This color-coding scheme allows users to visually examine relationships between attributes, even between those that are displayed far apart, without repositioning or reordering axes. We adopt the Visual Information Seeking Mantra so that the polylines of the original PCP can be used to show details of a small number of selected items when the cluttering problem subsides. We also design interactions, such as a focus+context technique, to help users investigate small regions of interest in a space-efficient manner. We provide a real-world example in which PHP is effectively utilized compared with other visualizations, and we perform a controlled user study to evaluate the performance of PHP in helping users estimate the correlation between attributes. The results demonstrate that the performance of PHP was consistent in the estimation of correlations between two attributes regardless of the distance between them.
Reversible data hiding hides data in an image such that the original image is recoverable. This paper presents a novel embedding framework with reduced distortion called skewed histogram shifting ...using a pair of extreme predictions. Unlike traditional prediction error histogram shifting schemes, where only one good prediction is used to generate a prediction error histogram, the proposed scheme uses a pair of extreme predictions to generate two skewed histograms. By exploiting the structure of the skewed histogram, only the pixels from the peak and the short tail are used for embedding, which decreases the distortion from the lesser number of pixels being shifted. Detailed experiments and analysis are provided using several image databases.
Pedestrian detection is a key problem in computer vision and is currently addressed with increasingly complex solutions involving compute-intensive features and classification schemes. In this scope, ...histogram of oriented gradients (HOG) in conjunction with linear support vector machine (SVM) classifier is considered to be the single most discriminative feature that has been adopted as a stand-alone detector as well as a key instrument in advance systems involving hybrid features and cascaded detectors. In this paper, we propose a pedestrian detection framework that is computationally less expensive as well as more accurate than HOG-linear SVM. The proposed scheme exploits the discriminating power of the locally significant gradients in building orientation histograms without involving complex floating point operations while computing the feature. The integer-only feature allows the use of powerful histogram inter-section kernel SVM classifier in a fast lookup-table-based implementation. Resultantly, the proposed framework achieves at least 3% more accurate detection results than HOG on standard data sets while being 1.8 and 2.6 times faster on conventional desktop PC and embedded ARM platforms, respectively, for a single scale pedestrian detection on VGA resolution video. In addition, hardware implementation on Altera Cyclone IV field-programmable gate array results in more than 40% savings in logic resources compared with its HOG-linear SVM competitor. Hence, the proposed feature and classification setup is shown to be a better candidate as the single most discriminative pedestrian detector than the currently accepted HOG-linear SVM.
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