Pixel Value Ordering Research Articles

Enhanced pairwise IPVO-based reversible data hiding scheme using rhombus context

Pixel-Value-Ordering (PVO) based reversible data hiding schemes provide high-fidelity stego-images with moderate embedding capacity. In this paper, a novel reversible data hiding scheme based on enhanced pairwise pixel value ordering is proposed to increase embedding capacity. The proposed scheme divides a host image into non-overlapping blocks of three pixels in zig-zag order and categorizes the blocks into two categories namely absolute smooth and probably complex based on the rhombus mean for each pixel of the block. In the case of absolute smooth, the pairwise embedding is done in two-pass using two different predictors. In the pass-1, enhanced pairwise improved PVO method is used to embed the secret data in the block. The proposed method decreases the minimum valued pixels and increases the maximum valued pixels for embedding the secret data. In the pass-2, secret data is hidden using a novel recovery based pairwise embedding strategy, where the value of the first pixel according to the sorted mean sequence will be either increased or remains unchanged while the value of the last pixel will be either decreased or remains unchanged. Additionally, the medium pixel is also used to embed the secret data by modifying its value in any direction using prediction error expansion. Therefore, the pass-2 embedding is done in such a way that it becomes the complement of pass-1 embedding so that stego-image quality can be maintained with additional embedded data. In case of probable complex blocks, the secret data is embedded in pixel wise manner to local complexity of each pixel. The use of rhombus mean in block categorization allows embedding in each and every pixel of the host image while ensuring its reversibility. Thus, the proposed scheme significantly increases the embedding capacity. The experimental results show that the proposed scheme has almost doubled on the embedding capacity than the previous PVO-based reversible data hiding schemes. Further, it has higher PSNR at high embedding capacity.

Pixel Value Ordering Based Reversible Data Hiding with Novel MPBS Strategy

Recently, Reversible Data Hiding (RDH) techniques has gained much attention in many sensitive fields such as remote sensing, archive management, military and medical image processing systems. This is due to the lossless data extraction ability of RDH schemes. The primary goal of RDH schemes is to achieve high embedding rates while maintaining the quality of cover objects. For achieving better performance, Pixel Value Ordering (PVO) based reversible data hiding schemes have been proposed. PVO refers to the process of ranking the pixels in blocks and then modifying the pixels according to some embedding rules/conditions. So far, the existing PVO techniques have considered neighborhood pixels at unit distance. In this paper, an improved RDH using block based PVO scheme is proposed which exploits the pixel correlation efficiently by increasing the block size and applying a novel Median Pixel based Block Selection Strategy (MPBS). When block size is increased, the ordering of pixels is changed after embedding. So, to extract the secret data in a lossless manner, the secret bits are swapped in accordance with their corresponding Stego pixels’ index order. Also, the overflow and underflow conditions are effectively handled using Location Map. Experimental results show the better performance of the proposed RDH technique with the existing technique.

Flexible spatial location-based PVO predictor for high-fidelity reversible data hiding

An advanced predictor offers the most effective way for performance enhancement of prediction-error expansion (PEE) based reversible data hiding. Unlike conventional predictors which exploit the correlation among adjacent pixels, pixel-value-ordering (PVO) exploits the one between the largest/smallest two pixels within block regardless of where they are located. Later, the spatial location received the attention from Peng et al. and led to better expansion bins selection. In this paper, a flexible spatial location (FSL) strategy is proposed to optimize the utilization of spatial correlation in PVO prediction. With FSL, pixels within block are no longer collected in a fixed way (e.g., from top to bottom, left to right). Specifically, eight modes of defining spatial location are designed for pixel collection. After pixel sorting, the inverse number of the location sequence is used to evaluate all modes and determine the best one. By determining the optimal mode for each block, the prediction becomes adaptive and thus a prediction-error histogram with higher expansion bin will be obtained. Numerous PVO-based schemes will benefit from FSL. In this paper, how FSL is applied to conventional PEE and high-dimensional PEE is also introduced. Experimental results demonstrate that FSL-PVO is of great significance to better capacity-distortion trade-off.

An Insight into Pixel Value Ordering Prediction Based Prediction-error Expansion

As the core of prediction-error expansion technique, prediction method has a fundamental impact on performance of reversible data hiding. Pixel value ordering (PVO) prediction has been extensively investigated for its high accuracy. However, the correlation of pixels within block has not been fully exploited yet. In this paper, a novel prediction method is proposed by developing PVO prediction in the aspects of spatial correlation and correlated pixel pair. The key to PVO embedding is invariant pixel value order such that the predicted pixel can be identified by value. Instead of predicting and enlarging the largest pixel, we propose to predict and reduce the second largest one and even all others. As the largest pixel which serves as predicted value is maintained after embedding, the numerous predicted pixels can be identified and thus reversibility is guaranteed. IPVO prediction which location-dependently determines the predicted pixel is also developed. For further optimization, multi-pass IPVO embedding is extended from single-layered to double-layered such that full-enclosing pixels can be used to estimate pixel distribution and determine the optimal mode of defining spatial location. Finally, an enhanced pairwise PEE is incorporated with multi-pass IPVO for performance enhancement. Experimental results show that the proposed scheme not only outperforms PVO embedding and its miscellaneous extensions, but also achieves significant superiority in fidelity over a series of state-of-the-art schemes.

Location-Based PVO and Adaptive Pairwise Modification for Efficient Reversible Data Hiding

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified.

A Hybrid Reversible Data Hiding for Multiple Images With High Embedding Capacity

This paper proposed a high capacity reversible data hiding (RDH) scheme using three steganographic images, on which three successive embedding phases are performed. An embedding rule is elaborately designed to guide the data embedding and three alike stego-images are generated in the first phase. In view of the similarity of three stego-images, the pixel value ordering (PVO) strategy is utilized in the second phase. In the third phase, the prediction error histogram shifting (PEHS) strategy is employed by exploiting the slight difference among three temporary stego-images. Since no complex operation is involved, the algorithm is efficient. The experimental results verify that our method is superior to other schemes in both EC and image quality. Moreover, the proposed scheme can successfully resist the RS steganalysis and provide a secure secret transmission.

Adaptive Complexity for Pixel-Value-Ordering Based Reversible Data Hiding

Pixel-value-ordering (PVO) is a widely used reversible data hiding (RDH) framework which aims to achieve the high quality of stego-image under low capacity. In this letter, we propose a general location-based adaptive complexity for PVO. Different from the block-based complexity in the previous PVO-based methods, our proposed method adaptively selects context pixels from the perspective of the relative locations of predicted pixel and prediction pixel. Consequently, different number of high correlation context pixels can be adaptively selected and the context pixels can break the limitation of the current block. Moreover, instead of sharing the same block complexity by two predicted pixels in the current block, each predicted pixel can be utilized independently according to its own corresponding complexity. Our proposed adaptive complexity can combine with any PVO-based methods and the experimental results show that our proposed method achieves a significant improvement in prediction accuracy and embedding performance.

A generalized pixel value ordering data hiding with adaptive embedding capability

In this paper, we proposed an improvisation to Li et al.’s pixel value ordering (PVO) method and called it as generalized PVO (GePVO), which provides a suitable expansion strategy using the image redundancy more abundantly. GePVO makes use of a reference pixel and calculates prediction errors using both sides of the block in different modes (one-way GePVO, bidirectional GePVO, round-trip GePVO). The experimental results demonstrate that GePVO had better performance in terms of an increased embedding capacity compared to the other data hiding methods. When compared to Li et al.’s method, the bidirectional GePVO led to an increase in the embedding capacity by 1.8 times and the round-trip GePVO by three times. We contend that the GePVO method made the PVO method more flexible and unbound in terms of embedding capacity while keeping the image quality intact. Performance comparison with other reversible data hiding schemes is presented to demonstrate the validity of the proposed method.

Reversible Data Hiding by Using Adaptive Pixel Value Prediction and Adaptive Embedding Bin Selection

In this letter, a reversible data hiding (RDH) scheme by using adaptive pixel value prediction and adaptive embedding bin selection based on pixel-based pixel value ordering (PPVO) is proposed. Different from the previous PPVO based methods, each to-be-embedded pixel is predicted by its neighbor pixels, which are selected adaptively according to the complexity of its neighbor pixel values. Moreover, the value range of embedding bin is not constrained in our method. Especially, when the value of embedding bin is less than 0, its value is updated adaptively during the process of embedding and extracting secret bits. Our maximum embedding capacity (EC) is improved significantly due to unconstrained embedding bin. In addition, the optimal embedding bins can be selected to achieve highest visual quality under a given EC. Experimental results show that the proposed method outperforms some state-of-the-art PPVO based RDH methods.

Improved PPVO-based high-fidelity reversible data hiding

Most pixel-value-ordering (PVO) based reversible data hiding (RDH) methods conduct pixel prediction on image blocks, while a recently proposed pixel-based PVO (PPVO) method changes the blockwise prediction in PVO to pixelwise manner and achieves better embedding performance. However, in PPVO, the prediction is not accurate since some pixels close to the to-be-predicted one are not utilized. Moreover, pixels with different local complexity are not fully exploited and the embedding performance of PPVO is not optimized. Thus, to better determine the prediction context as well as full use the image local correlation, an improved PPVO-based RDH method is proposed in this paper. First, to improve the prediction accuracy, a new predictor is proposed in which the prediction context is properly selected. Then, for the optimized performance, a new embedding strategy is proposed based on multiple histograms generation and modification with multi-sized prediction contexts. Experimental results verify that the proposed method is superior to PPVO and some other state-of-the-art RDH methods.

Reversible data hiding based on novel pairwise PVO and annular merging strategy

Reversible data hiding (RDH) methods aim to reduce the embedding distortion below the required embedding capacity. Concerning this problem, pixel value ordering (PVO)-based methods achieve remarkably good performance as they only modify the minimum and maximum of pixels in each block. In this paper, we further exploit the embedding potential of PVO by merging blocks that cannot be directly used in PVO with neighboring blocks, in order to generate a larger block. The mechanism of PVO is reconsidered and pixel distribution density (PDD) is defined as a metric to control both order and termination of merging strategy. Moreover, a novel two-dimensional prediction-error histogram is generated to enhance the embedding performance. Experimental results verify that the proposed method outperforms previous PVO-based schemes and state-of-the-art methods.

Reversible data hiding based on novel embedding structure PVO and adaptive block-merging strategy

In data hiding field, pixel-value-ordering (PVO)-based methods yield excellent performances by taking full advantage of correlation in a fixed block. In PVO-based methods, the maximum and minimum of each block are utilized to embed data. The maximal embedding capacity (EC) is achieved by setting embedding block size smaller to generate more basic embedding blocks. In this light, a new three-pixel embedding structure is proposed to improve the maximal EC by reducing the number of pixels in each basic block. Meanwhile, unlike conventional algorithms which take the noise level of current block as complexity, we utilize the correlation information among neighboring blocks to get a better classification on embedding blocks. Moreover, dynamic block partition method is further investigated and an adaptive block-merging strategy is proposed in this paper. In this strategy, a more rational way is provided to exploit the embedding potential than the top-down partition method and the performance is significantly enhanced. With these three new improvements, our scheme exhibits better performance than the state-of-the-art PVO-based methods.

Directional pixel value ordering based secret sharing using sub-sampled image exploiting Lagrange polynomial

In this paper, we proposed secret sharing within the sub-sampled image using Directional Pixel Value Ordering (DPVO) for Reversible Data Hiding scheme. The original cover is partitioned into sub-sampled images which extend its size through image interpolation technique. The secret information has been converted through Lagrange interpolation polynomial techniques. These new secret data is embedded within pixels of each interpolated sub-sampled images using DPVO. In the decoder side, the secret data is taken out from the pixels of every stego image using reverse DPVO. After that, Lagrange’s interpolation is applied to generate the original secret message. The proposed scheme enhanced the security for the sharable characteristics of secrets amongst several images. It raises the data embedding capacity and improves the visual quality that is determined by peak signal to noise ratio (PSNR). The average PSNR value is over 60 dB. It shows the superiority of the new algorithm over the other existing data hiding methods regarding payload of data, quality, and security of the image. Also, the stego images have evaluated through standard deviation, regular singular analysis, correlation coefficient, normalized cross-correlation and structural similarity index in between original cover and marked (stego) image to display the robustness of our result among the several steganographic strikes.

Overlapping pixel value ordering predictor for high-capacity reversible data hiding

In recent years, many reversible information hiding methods have been proposed. Among them, the pixel value ordering (PVO) method can be used to create high-fidelity camouflage images under good embedding capacity. The original PVO method adopted the block-by-block manner, and each block could embed only 2 bits. In this study, we propose an overlapping PVO (OPVO) method. In the PPVO method, secret data are embedded in the maximum and minimum pixel values to improve the situation in which only up to 1 bit can be embedded in a sliding window. In the pixel-by-pixel manner, each pixel can embed data multiple times. We make full use of the correlation between adjacent pixels in the natural image to increase the embedding capacity. An advantage of the overlapping PVO method is that its embedding capacity is more than twice as high as that of previous PVO series methods where an acceptable image quality is maintained. In our experiment, the embedding capacity of the smooth image is up to 130,000 bits, whereas that of the complex image is higher. However, the image quality decrease rate of the proposed method tends to be lower than that of the other methods. Therefore, any kind of image is suitable for our method.

High-capacity reversible image steganography based on pixel value ordering

In this study, an improved reversible image steganography method based on pixel value ordering (PVO) is proposed to improve the steganographic capacity. Before the steganography process, three continuous and neighboring pixels are regarded as one group for sequencing, where the maximum and minimum values are adopted for difference value calculation and the number of difference values is recorded. The pixels with more steganographic difference values in rows or columns are determined, after which the steganography and ciphertext retrieval steps are performed in the rows or columns of the digital image. The experiment results prove that the method replaces the block structure in frame selection by groups of continuously read pixels, i.e., every three pixels form a pixel group where two bits of confidential information can be hidden, thus improving the steganographic capacity effectively. Comparisons with other PVO hiding schemes confirm the superiority of the proposed scheme, which has a higher capacity and maintains an acceptable peak signal-to-noise ratio (PSNR).

Dynamic improved pixel value ordering reversible data hiding

The existing reversible data hiding (RDH) methods based on improved pixel value ordering (IPVO) use only a small portion of the pixels (e.g., at most six pixels) in a block for embedding data. Our proposed method is a dynamic IPVO RDH, which can flexibly modify the number of pixels in a block by classifying the local complexity into multiple levels. For a highly-correlated, smooth block, almost all of the pixels, except for one or two in the middle positions, can be involved in embedding data. For a lowly-correlated smooth block, only two pixels can be embedded with data, and no data can be embedded into a highly-textured block. Thus, it is apparent that the redundancy among pixels is fully exploited, and further, more data can be embedded into smooth blocks while maintaining lower distortion. However, when all of the levels are used for embedding data, the visual quality may not be maximized for a given payload. Hence, in our method, we traverse from the first level to the highest level to search for the optimal number of levels that can provide the best embedding performance. Extensive experiments have demonstrated that the proposed method outperforms the existing methods.

Improved reversible data hiding based on PVO and adaptive pairwise embedding

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the cover image is first divided into non-overlapping blocks with equal size. Then, the pixel values in each block are sorted in ascending order. Next, take the second largest/samllest pixel value as a prediction of the largest/samllest pixel value to derive two prediction errors. Finally, the data embedding is constructed by modifying the generated prediction errors of each block. After data embedding, the PVO of each block is unchanged, which guarantees the reversibility. Our key observation is that, in each block, the modification for the two prediction errors is independent without exploiting the correlation between them, although they are closely correlated with each other. In light of this, an improved PVO-based RDH method is proposed in this work. The two prediction errors of each block are considered as a pair, and the pairs are modified for data embedding based on adaptive two-dimensional histogram modification. The proposed method is experimentally verified better than the original PVO-based method and some of its improvements.

Reversible Data Hiding Scheme Based on Quad-Tree and Pixel Value Ordering

This study proposes a reversible data hiding (RDH) scheme based on pixel value ordering (PVO). RDH schemes based on PVO technique generally realize the prediction procedure by employing the sorted pixels in a block-wise manner, and then embedding secret data by expanding the prediction errors. Since these operations are carried out based on the block, and pixel intensity has spatial correlation such that the neighboring pixels have similar values, the block patterns and sizes certainly affect the embedding performance of the PVO-based RDH method. The original PVO-based RDH schemes use fixed block size. However, due to the differences in image complexity, fixed-size blocks have many limitations. Thus far, several methods that break the limitations have been proposed, such as setting a threshold value, and determine the size from the complexity of the block, using sliding window instead of fixed blocks. These methods significantly enhanced the embedding performance of PVO-based RDH schemes, but this can still be improved. The proposed scheme introduces the quad-tree structure, which combines the advantages of several previously proposed methods to design a flexible block patterns, thus fully utilizing the characteristics of the image itself in embedding secret data. Experimental results indicate that the proposed scheme is flexible and better than previous approaches.

Secure Gray code-based reversible data hiding scheme in radiographic images

Transmitting medical information through a network for the purpose of tele-diagnosis involves greater risk of losing confidentiality and integrity of the information being transmitted. This paper presents a scheme that ensures reversibility of the cover image and also makes it suitable for the field of telemedicine. The methodology uses cryptographic and the steganographic methods. The proposed work decreases the overhead by reducing the size of the auxiliary data to be embedded which is used to achieve the reversibility of the cover image. The proposed method also improves security of the data and enhances the image quality. The algorithm yields a reversible data hiding (RDH) scheme based on pixel value ordering (PVO). The methodology differs from other basic schemes as it uses Gray code instead of ordinary binary codes. It naturally suits for medical steganography as the carrier image can be reconstructed after extraction of the secret data and also the distortion caused due to embedding is very less. The method is also robust as one time pad cryptographic technique is used to generate the key.

Improved K-Pass Pixel Value Ordering Based Data Hiding

K-pass pixel value ordering (PVO) is an effective reversible data hiding (RDH) technique. In k-pass PVO, the complexity measurement may lead to a weak estimation result because the unaltered pixels in a block are excluded to estimate block complexity. In addition, the prediction-error is computed without considering the location relationship of the second largest and largest pixels or the second smallest and smallest pixels. To this end, an improved RDH technique is proposed in this paper to enhance the embedding performance. The improvement mainly lies in the following two aspects. First, some pixels in a block, which are excluded from data hiding in some existing RDH methods, are exploited together with the neighborhood surrounding this block to increase the estimation accuracy of local complexity. Second, the remaining pixels in a block, i.e., three largest and three smallest pixels are involved in data embedding. Taking three largest pixels for example, when the difference between the largest and third largest pixels is relatively large (e.g., > 1), we improve k-pass PVO by considering the location relationship of the second largest and largest pixels. The advantage of doing this is that the difference valued 3 between the maximum and the second largest pixel which is shifted in k-pass PVO, is able to carry 1 bit data in our method. In other words, a larger amount of pixels are able to carry data bits in our scheme compared with k-pass PVO. Abundant experimental results reveal that the proposed method achieves preferable embedding performance compared with the previous work, especially when a larger payload is required.