Prediction Error Histogram Research Articles

Reversible data hiding based on hybrid prediction and interleaving histogram modification with single seed pixel recovery

This paper proposes a reversible data hiding scheme for natural images. A hybrid prediction mechanism is utilized in order to produce prediction errors as many as possible. The cover image excluding a seed pixel is partitioned into four non-overlapping segments, and four predictors are tailored for each of them. As a result, most prediction errors concentrate around zero in prediction error histogram. Besides, an interleaving histogram modification mechanism is presented such that the capacity is enhanced and easier to be finely tuned in contrast to some previous approaches. Third, a single seed pixel recovery strategy is introduced. Experimental results show the effectiveness of the proposed method.

Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection

Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values. Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is obtained and a better visual quality of watermarked image is observed. With these improvements, our method outperforms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.

A local variance-controlled reversible data hiding method using prediction and histogram-shifting

The stego image quality produced by the histogram-shifting based reversible data hiding technique is high; however, it often suffers from lower embedding capacity compared to other types of reversible data hiding techniques. In 2009, Tsai et al. solved this problem by exploiting the similarity of neighboring pixels to construct a histogram of prediction errors; data embedding is done by shifting the error histogram. However, Tsai et al.’s method does not fully exploit the correlation of the neighboring pixels. In this paper, a set of basic pixels is employed to improve the prediction accuracy, thereby increasing the payload. To further improve the image quality, a threshold is used to select only low-variance blocks to join the embedding process. According to the experimental results, the proposed method provides a better or comparable stego image quality than Tsai et al.’s method and other existing reversible data hiding methods under the same payload.

An Efficient Prediction-and-Shifting Embedding Technique for High Quality Reversible Data Hiding

The embedding capacity of a histogram-based reversible data hiding technique is primarily determined by the peak height of the histogram. Recently, some studies have tried to embed data in the histogram of prediction errors by modifying the error values and have better embedding efficiency. However, these methods offer no selective embedment mechanism to exclude the positions where the modification in the embedding operation contributes no capacity but merely degrade the image quality. In this paper, a novel coding method for reversible data hiding is presented. A two-stage prediction algorithm that fully exploits the pixel correlations is employed to create more embeddable spaces, and a selective embedment mechanism is used to enhance the image quality. According to the experimental results, the proposed method achieved the highest payload while maintaining the lowest distortion for most standard test images, comparing to other existing histogram-shifting-based reversible data hiding techniques.

A high capacity reversible data hiding scheme using orthogonal projection and prediction error modification

The payload of a histogram-shifting based reversible data hiding technique is primarily determined by the peak height of the corresponding image histogram. It is known that the peak height of the prediction error histogram is usually higher than that of image histogram itself. In 2009, Tsai et al. adopted the idea of histogram-shifting technique and embedded message by modifying the prediction errors to achieve a higher payload. They used a block-based approach and leaved some pixel values unmodified to predict other pixel values in a block. However, the unmodified pixels took up some embeddable space, causing a reduction in payload. In this paper, a new reversible data hiding technique based on prediction error modification is proposed. The orthogonal projection technique is employed to estimate the optimal weights of a linear predictor to increase the prediction accuracy and the embedding capacity. The experimental results revealed that the proposed method outperformed the existing histogram-shifting based reversible data hiding technique in terms of payload and image quality.

Subjectively adapted high capacity lossless image data hiding based on prediction errors

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.

Reversible data hiding for high quality images using modification of prediction errors

In this paper, a reversible data hiding scheme based on modification of prediction errors (MPE) is proposed. For the existing histogram-shifting based reversible data hiding techniques, though the distortion caused by embedding is low, the embedding capacity is limited by the frequency of the most frequent pixel. To remedy this problem, the proposed method modifies the histogram of prediction errors to prepare vacant positions for data embedding. The PSNR of the stego image produced by MPE is guaranteed to be above 48 dB, while the embedding capacity is, on average, almost five times higher than that of the well-known Ni et al. techniques with the same PSNR. Besides, MPE not only has the capability to control the capacity-PSNR, where fewer data bits need less error modification, but also can be applied to images with flat histogram. Experimental results indicate that MPE, which innovatively exploits the modification of prediction errors, outperforms the prior works not only in terms of larger payload, but also in terms of stego image quality.