Pixel Value Ordering Research Articles

Pixel-Value-Ordering based Reversible Information Hiding Scheme with Self-Adaptive Threshold Strategy

Pixel value ordering (PVO) hiding scheme is a kind of data embedding technique that hides a secret message in the difference of the largest and second largest pixels of a block. After that, the scholars improved PVO scheme by using a threshold to determine whether the block is smooth or complex. Only a smooth block can be used to hide information. The researchers analyzed all the possible thresholds to find the proper one for hiding secret message. However, it is time consuming. Some researchers decomposing the smooth block into four smaller blocks for hiding more messages to increase image quality. However, the complexity of the block is more important than block size. Hence, this study proposes an ameliorated method. The proposed scheme analyzes the variation of the region so as to judge the complexity of the block and applies quantification strategy to quantified the pixel for making sure the pixel is reversible. It adopts an adaptive threshold generation mechanism to find the proper threshold for different images. The results show that the image quality of the proposed scheme is higher than that of the other methods. The proposed scheme can also let the user adjust the hiding rate to achieve higher image quality or hiding capacity.

Reversible data hiding using the dynamic block-partition strategy and pixel-value-ordering

The reversible watermarking algorithm based on pixel value ordering (PVO) embeds secret data by modifying the maximum and minimum values in a pixel block. The performance of this algorithm heavily depends on the inherent correlation among adjacent pixels within an image block. To improve the inherent correlation among adjacent pixels within an image block, a new method to incorporate the dynamic block-partition strategy into the PVO algorithm is proposed in this paper. This new method includes the following procedure: First, the host image is divided into non-overlapping regions according to the image pixel values; then, each region is partitioned into different blocks, which are subsequently classified according to the local complexity and predefined threshold values for embedding; and finally, watermark embedding is performed using the PVO-based algorithm. In the proposed method, the pixel values of each embedded block are located in a relatively small region to improve the inherent correlation and thereby enhance the embedding performance of PVO. The experimental results show that the proposed algorithm has a better embedding performance compared with that of the conventional PVO based algorithm.

High-fidelity reversible data hiding by Quadtree-based pixel value ordering

Recently, the pixel value ordering (PVO) method has received a great deal of attention in the field of high-fidelity reversible data hiding. To improve the embedding performance of the PVO-based method, a quadtree-based pixel value ordering (QPVO) method based on dynamic quadtree partition is proposed in this paper. Instead of using equal-sized blocks, blocks in various sizes are adaptively generated based on the quadtree partitioning and block complexity. Smooth regions are divided into smaller blocks to obtain high embedding capacity, while rough regions are divided into larger blocks to avoid distortion. The decoder can recover the original image as well as the original quadtree structure by the block complexity. Extensive experiments demonstrate that the proposed QPVO method could significantly improve the embedding performance of these PVO-based methods, especially for a relatively small embedding payload.

Locally Adaptive Rank-Constrained Optimal Tone Mapping

High dynamic range (HDR) tone mapping is formulated as an optimization problem of maximizing perceivable spatial details given the limited dynamic range of display devices. This objective can be attained, as supported by our results, by a novel image display methodology called locally adaptive rank-constrained optimal tone mapping (LARCOTM). The scientific basis for LARCOTM is that the maximum discrimination power of human vision system can only be achieved in a relatively small locality of an image. LARCOTM is fundamentally different from existing HDR tone mapping techniques in that the former can preserve pixel value order statistics within localities in which human foveal vision retains maximum sensitivity, while the latter cannot. As a result, images enhanced by LARCOTM are free of artifacts such as halos and double edges that plague other HDR methods.

ENHANCEMENT OF PIXEL VALUE ORDERING BASED DATA HIDING BY ROW BLOCK PARTITION

The development of information and communication technology that support digital data transmission such as text, image, audio and video gives several effects. One of them is data security that becomes the main priority during the transmission process. Pixel-Value-Ordering (PVO) which one of data hiding methods can be implemented to achieve the requirement. It embeds data on maximum pixel and minimum pixel in a blok which is a part of the carrier image. However, PVO has capacity a problem, that only 2 bits per block can be hidden. To handle this problem, we propose a new approach by dividing blocks dinamically based on its complexity. These blocks are grouped into 4: smooth block, semi-smooth block, normal block and rough block. Using this approach, the stego capacity can be improved up to 2.6 times in average of previous method by keeping the quality stego more than 65 dB for all testing image.

Directional PVO for reversible data hiding scheme with image interpolation

Pixel Value Ordering (PVO) is an efficient data hiding scheme where pixels are ranked in ascending order within an image block and then modify minimum and maximum pixel value to embed secret data. The embedding capacity of existing PVO based data hiding schemes were limited to embed only two bits in a row of any block and unable to perform repeated embedding. To solve the existing problem, we have proposed a generalized directional PVO (DPVO) with varying block size. The original image is partitioned into blocks and then enlarged using image interpolation. A new parameter (α) is introduced and added with maximum pixel value and subtracted from minimum pixel value to maintain the order of the rank which is dependent on the size of the image block. To improve data hiding capacity, overlapped embedding has been considered in three different directions (1) Horizontal, (2) Vertical and (3) Diagonal of each block. Experiments show that the proposed scheme has a good margin of performance compared with the state-of-the-art methods. Several steganographic analysis deemed robust against several attacks.

Reversible data hiding using multi-pass pixel-value-ordering and pairwise prediction-error expansion

Pixel value ordering (PVO) prediction can achieve remarkable accuracy and thus provide a rather sharp histogram. In addition, the efficiency in histogram manipulation also attracts much attention in recent works. In this paper, a new reversible data hiding scheme based on multi-pass PVO and pairwise PEE is proposed. After dividing the host image into non-overlapped blocks, the largest/smallest two pixels within block are predicted to form a prediction-error pair and finally a 2D prediction-error histogram. Here, the third largest/smallest pixel no longer always serves as predicted value. Once any one error in a pair is shifted, we propose to adaptively re-calculate the other one. For smooth block, location information is considered and then more expandable errors are obtained. For normal block, the shifted pixel is involved in prediction and shifting two errors in a pair without carrying any bit can be avoided. Such multi-pass prediction leads to the so-called high and low power version of PVO-based pairwise PEE. Experimental results verify that their combination can achieve very efficient capacity-distortion trade-off and thus outperform previous PVO-based schemes.

Reversible data hiding using multi-pass pixel value ordering and prediction-error expansion

Pixel value ordering (PVO) prediction has become the most efficient method for high-fidelity reversible data hiding (RDH). In this approach, only the maximum and minimum of pixel block are predicted and modified to embed data and the preservation of pixel value order guarantees the reversibility. To achieve larger embedding capacity and superior performance, more blocks suitable for RDH are utilized in recent improved schemes. However, their performance is still unsatisfactory. In this paper, a novel RDH scheme is proposed by extending original PVO into multi-pass PVO embedding. Specially, the k largest or smallest pixels are taken as independent data bit carriers to fulfill k-pass PVO embedding. Although the pixel value order may change after data embedding, reversibility still can be guaranteed and image redundancy can be far better exploited. Moreover, embedding performance can be further enhanced by optimal combined embedding. Experimental results verify that the proposed scheme outperforms previous PVO-based schemes and some other state-of-the-art works.

Pairwise IPVO-based reversible data hiding

Recently, Peng et al. proposed a reversible data hiding method based on improved pixel-value-ordering (PVO) and prediction-error expansion. In this paper, a novel method is proposed by extending Peng et al.’s work. In our method, three largest (or smallest) pixels in a block are utilized to generate two differences, and a new pixel modification strategy is proposed so that the PVO remains unchanged after data embedding. Taking three largest pixels for example, we utilize the third largest pixel to predict the second largest one, and meanwhile use the second largest one to predict the maximum. In this way, two differences are obtained. They are modified jointly so as to be embedded with log 23 bits instead of 2 bits in the traditional RDH methods. The advantage of doing so is to exclude situations where PVO is changed. Moreover, two embedding layers are utilized together to further decrease the embedding distortion. Extensive experiments verify that the proposed method outperforms Peng et al. ’s and some other state-of-the-art works.

Efficient PVO-based reversible data hiding using multistage blocking and prediction accuracy matrix

In recent years, pixel value ordering based reversible data hiding has become a hot research topic for its high-fidelity. In this approach, only the maximum and minimum of pixel block are predicted and modified to embed data and the preservation of pixel values order guarantees the reversibility. So far, the optimal block size can only be exhaustively searched until Wang et al. propose the dynamic blocking strategy which enables the combination of two various-sized blocks. By further dividing flat block into four sub-blocks to retain larger embedding capacity, dynamic blocking can employ less high complexity blocks for a given embedding capacity. However, the lack of host image dependent automatic block classification mechanism still exposes the fact that their work is far from efficient and comprehensive. In this paper, to address this drawback and to better exploit image redundancy, a really efficient and more comprehensive blocking strategy namely multistage blocking is proposed. High efficiency lies in prediction accuracy matrix based thresholds determination, which enables infinitely extended multistage blocking in theory. The superiority of the proposed scheme is also experimentally verified.

Separable reversible data hiding in encrypted image based on pixel value ordering and additive homomorphism

This work proposes a separable reversible data hiding scheme in encrypted images based on pixel value ordering (PVO). After the original image is encrypted using homomorphism encryption by the content owner, the data hider embeds the secret data in encrypted domain. The PVO strategy realizes hiding data in each block. Additive homomorphism guarantees the performance of PVO in encrypted domain is close to that in plain domain. Besides, the homomorphism encryption does not cause data expansion, and the payload can be further improved. With the watermarked encrypted image, if the receiver has only the data hiding key, he can extract the additional data. If the receiver has only the encryption key, he can obtain a decrypted image similar to the original one. If the receiver has both the data hiding key and the encryption key, he can extract the additional data without any error and recover the original image losslessly.

Reversible data hiding based on an adaptive pixel-embedding strategy and two-layer embedding

Recently, Peng et al. proposed a reversible data-hiding (RDH) method based on pixel value ordering (PVO) and prediction-error expansion. In their method, the maximum and minimum of a pixel block are predicted and modified to embed data, and reversibility is guaranteed by keeping the PVO of each block invariant after embedding. In this paper, a novel RDH method is proposed by incorporating an adaptive pixel-modification strategy into Peng et al.’s work. In our method, the smoother a block, the more data it can carry. Unlike Peng et al.’s method, which embeds data uniformly, ours can embed data (e.g., 2, 4, or 6 bits) adaptively into a block according to the local complexity. Specifically, the local complexity is classified into four levels according to the strength of the correlation between each block and its neighborhood. Then the number of pixels to be modified is determined by the complexity level such that more bits are embedded into a block located in a smoother region. Moreover, two embedding layers are utilized together to further decrease the embedding distortion. Extensive experiments verify that the proposed method outperforms Penget al.’s, Ou et al.’s, Li et al.’s, Sachnev et al.’s, and Hong et al.’s works.

High-fidelity reversible data hiding based on pixel-value-ordering and pairwise prediction-error expansion

Pixel-value-ordering (PVO) technique refers to the process of first ranking the pixels in a block and then modifying the maximum/minimum for reversible data hiding (RDH). This paper discusses the PVO embedding in two-dimensional (2D) space and utilizes the prediction-error pair within a block for data embedding. We focus on not only the exploitation of conventional PVO embedding but also its effective implementation in 2D form. The PVO embedding is extended into a 2D form by integrating the pairwise prediction-error expansion, and a reversible 2D mapping adapted to the special distribution of prediction-error pairs is proposed. Moreover, an adaptive mapping selection mechanism is proposed to treat separately rough and smooth prediction-error pairs to further optimize the embedding performance. Experimental results show that the proposed method outperforms the previous PVO-based methods.

Improved PVO-based reversible data hiding: A new implementation based on multiple histograms modification

Pixel-value-ordering (PVO) technique is based on ranking the pixels within a block and often employed in reversible data hiding (RDH) to preserve the image fidelity. In this paper, an improved PVO-based algorithm is proposed to achieve a high-performance RDH. We formulate the PVO embedding as a problem of multiple histograms modification (MHM) and make two contributions. Firstly, the existing PVO-based algorithms are generalized in an MHM form and the systematic analysis of performance is offered by considering the parameter determination. Secondly, based on the new framework, an improved implementation of PVO embedding is proposed to adaptively determine the MHM manner by solving the distortion minimization of multiple histograms, which can handle different characteristics in image areas. Experimental results show the superiority of the new algorithm over the state-of-the-art methods.

PVO based Reversible Data Hiding with Improved Embedding Capacity and Security

Background/Objectives: Pixel Value Ordering (PVO) based embedding is a method for image based Reversible Data Hiding (RDH) to plant secret data in minimum and maximum pixel values of each block of the image. This paper serves as an extension of a recently proposed PVO scheme with higher security and embedding capacity. Methods/Statistical Analysis: In this proposed method the image is first divided into equal sized non-overlapping blocks. The pixels in the individual blocks are ordered according to their values and differences between the maximum and the second largest (or minimum and the second smallest) are computed. Then, histograms of these differences are modified to hide secret information. But original scheme rejects a lot of blocks belonging to textured regions of the image which can be used to embed even more data. Findings: The proposed scheme extends the original PVO method by using these rejected blocks to embed data thus increasing the Embedding Capacity. It is observed that although there is an increase in the overall embedding capacity, the degradation in the visual quality is negligible. It is also found that this increase in the capacity affects the robustness. So a simple randomization technique for the block selection process is also proposed. Applications/Improvements: This method can be applied for all the secret communication applications especially Defence, Telemedicine, etc. This proposed method developed further in terms of robust against various steganalysis tools.

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

Novel Reversible Pixel-Value-Ordering Technique for Secret Concealment

This work is to preserve the distortion of marked image from the original image, that is, to improve the Peak Signal-to-Noise Ratio (PSNR) and to improve the Embedding Capacity (EC). Pixel-Value-Ordering (PVO) is the technique used for ensuring reversibility in Reversible Data Hiding (RDH). The difference-value is calculated from ordered pixel blocks. From the difference-value histogram, the rough bins are shifted to vacate place for concealing secret whereas the smooth bins are expanded to conceal secret. The proposed Novel Reversible Pixel-Value-Ordering Technique (RPVOT) is the containment of two schemes: Novel Difference Computation (DC) and Novel Histogram Shifting (HS). Firstly, for DC, the existing approaches compute the difference between first two (or, last two) pixel values of an ordered block whereas, the novel DC technique picks up the first two pixel values (or, last two pixel values) from an ordered block and the rounded mean of pixel values in minimum and maximum position subtracted from a pixel value in minimum position is computed. This novel DC makes eligible more number of blocks for secret concealment than other existing approaches by which more secret bits can be concealed and visual worth of the marked image is conserved as well. Secondly, novel HS is proposed which break the rule of shifting all the blocks that are not meant for embedding by shifting some blocks using naturally occurring empty bins in the histogram. This novel HS prevents many blocks from unnecessary alteration in their pixel values and hence, visual worth of the marked image is improved further. Both novel DC and novel HS are combined together to form novel RPVOT which significantly improves the Embedding Capacity (EC) along with Peak Signal-to-Noise Ratio (PSNR).

Pixel-based pixel value ordering predictor for high-fidelity reversible data hiding

A pixel-value-ordering (PVO) predictor has been used in reversible data hiding methods to produce high-fidelity marked images when the payload size is moderate. The original PVO generates predictions in a block-by-block manner where only two pixels can be predicted in each block. In this paper, a novel pixel-based PVO (PPVO) is proposed with which each pixel is predicted using its sorted context pixels. The benefit of the PPVO is that the pixels in smooth image regions can be better used to embed data, while most of those pixels are neglected in PVO due to the block constraint. Experimental results demonstrate that the embedding capacity of PPVO is larger than that of PVO, especially for relatively smooth images. The obtained marked image quality of PPVO is also better than that of PVO and other state-of-the-art reversible data hiding methods.

Reversible data hiding using invariant pixel-value-ordering and prediction-error expansion

Recently, Li et al. proposed a reversible data hiding (RDH) method based on pixel-value-ordering (PVO) and prediction-error expansion. In their method, the maximum and the minimum of a pixel block are predicted and modified to embed data, and the reversibility is guaranteed by keeping PVO of each block invariant after embedding. In this paper, a novel RDH method is proposed by extending Li et al.׳s work. Instead of considering only a single pixel with maximum (or minimum) value of a block, all maximum-valued (or minimum-valued) pixels are taken as a unit to embed data. Specifically, the maximum-valued (or minimum-valued) pixels are first predicted and then modified together such that they are either unchanged or increased by 1 (or decreased by 1) in value at the same time. Comparing our method with Li et al.׳s, more blocks suitable for RDH are utilized and image redundancy is better exploited. Moreover, a mechanism of advisable payload partition and pixel-block-selection is adopted to optimize the embedding performance in terms of capacity-distortion behavior. Experimental results verify that our method outperforms Li et al.׳s and some other state-of-the-art works.

Improved PVO-based reversible data hiding

This work extends a recently proposed reversible data hiding (RDH) scheme of Li et al. which is based on pixel-value-ordering and prediction-error expansion. In Li et al.ʼs method, the maximum and minimum of a pixel block are predicted and modified to embed data. The pixel value order of each block is unchanged after data embedding and the property guarantees the reversibility. In this work, instead of the difference between the maximum and second largest value of a block (or, the minimum and second smallest value of a block) considered in Li et al.ʼs method, new differences are computed and new histogram-modification-strategy is utilized. Take the maximum for example, the new difference is defined considering the pixel locations of the maximum and second largest value. In this way, the blocks where the maximum equals to the second largest value can be exploited to embed data while these blocks suitable for RDH are not utilized in Li et al.ʼs work. This can better exploit image redundancy and achieve a superior embedding performance. Extensive experiments verify that the proposed method outperforms Li et al.ʼs and some other state-of-the-art works.