Reversible 3D Image Data Hiding with Quality Enhancement
17 Sep 2016-pp 446-455
TL;DR: A novel reversible data hiding method in the depth map for quality enhancement that can embed more data with less distortion by optimizing the selection of prediction error and embedding data with the help of depth no-synthesis-error model (D-NOSE).
Abstract: The 3D image constructed by a texture image plus a depth map is popular for stereo representation. When embedding data in the depth map, conventional reversible data hiding algorithms, which are designed for gray and color images, are not suitable. In this paper, we propose a novel reversible data hiding method in the depth map for quality enhancement. By optimizing the selection of prediction error and embedding data with the help of depth no-synthesis-error model (D-NOSE), the proposed scheme can embed more data with less distortion. The hidden data are the compressed residual error between the actual view and the virtual view. The watermarked depth map can directly be used to synthesis a same virtual view as in depth-image-based rendering (DIBR). After extracting the hidden data, a higher quality virtual view can be obtained by adding the residual error to the synthesized virtual view. Experimental results show that the virtual view with higher quality is obtained, and higher embedding capacity is achieved as well.
Citations
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Journal Article•
TL;DR: In this article, a reversible data hiding scheme based on histogram modification is proposed, where the binary tree structure is exploited to solve the problem of communicating pairs of peak points and the distribution of pixel differences is used to achieve large hiding capacity while keeping the distortion low.
Abstract: In this letter, we present a reversible data hiding scheme based on histogram modification. We exploit a binary tree structure to solve the problem of communicating pairs of peak points. Distribution of pixel differences is used to achieve large hiding capacity while keeping the distortion low. We also adopt a histogram shifting technique to prevent overflow and underflow. Performance comparisons with other existing schemes are provided to demonstrate the superiority of the proposed scheme.
12 citations
TL;DR: The proposed RDH method for three-dimensional (3D) synthetic images based on the depth-no-synthesis-error model outperforms the previous methods in terms of capacity and distortion, and a better 3D visual quality can be obtained.
Abstract: In this paper, we proposed a reversible data hiding (RDH) method for three-dimensional (3D) synthetic images based on the depth-no-synthesis-error model (D-NOSE). By using the depth-image-based rendering technique, the 3D image can be synthesized from a 2D color image and its corresponding depth map to provide the viewer a depth perception. Recently, a 3D-based RDH method is proposed to modify the depth map while still preserving the perfect 3D image synthesization. However, it has not fully exploited the pixel mapping relations in the synthesization, resulting in an insufficient capacity. Moreover, the optimal 3D visual quality is not discussed. To compensate this, we propose to improve the capacity and the 3D visual quality. The proposed method extends the pixels expansion toward two directions to fully exploit the allowable range, and can adjust the pixel modification to control the capacity. Furthermore, for a given capacity, the optimal baseline can be selected for a better 3D visual quality. Experimental results show that the proposed method outperforms the previous methods in terms of capacity and distortion, and a better 3D visual quality can be obtained as well.
6 citations
Posted Content•
TL;DR: Wang et al. as mentioned in this paper proposed a zero-watermark scheme based on invariant feature and similarity-based retrieval for protecting DIBR 3D video (RZW-SR3D).
Abstract: Digital rights management (DRM) of depth-image-based rendering (DIBR) 3D video is an emerging area of research. Existing schemes for DIBR 3D video cause video distortions, are vulnerable to severe signal and geometric attacks, cannot protect 2D frame and depth map independently or can hardly deal with large-scale videos. To address these issues, a novel zero-watermark scheme based on invariant feature and similarity-based retrieval for protecting DIBR 3D video (RZW-SR3D) is proposed in this study. In RZW-SR3D, invariant features are extracted to generate master and ownership shares for providing distortion-free, robust and discriminative copyright identification under various attacks. Different from traditional zero-watermark schemes, features and ownership shares are stored correlatively, and a similarity-based retrieval phase is designed to provide effective solutions for large-scale videos. In addition, flexible mechanisms based on attention-based fusion are designed to protect 2D frame and depth map independently and simultaneously. Experimental results demonstrate that RZW-SR3D have superior DRM performances than existing schemes. First, RZW-SR3D can extracted the ownership shares relevant to a particular 3D video precisely and reliably for effective copyright identification of large-scale videos. Second, RZW-SR3D ensures lossless, precise, reliable and flexible copyright identification for 2D frame and depth map of 3D videos.
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TL;DR: The redundancy in digital images is explored to achieve very high embedding capacity, and keep the distortion low, in a novel reversible data-embedding method for digital images.
Abstract: Reversible data embedding has drawn lots of interest recently Being reversible, the original digital content can be completely restored We present a novel reversible data-embedding method for digital images We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low
2,739 citations
TL;DR: It is proved analytically and shown experimentally that the peak signal-to-noise ratio of the marked image generated by this method versus the original image is guaranteed to be above 48 dB, which is much higher than that of all reversible data hiding techniques reported in the literature.
Abstract: A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.
2,240 citations
TL;DR: Details of a system that allows for an evolutionary introduction of depth perception into the existing 2D digital TV framework are presented and a comparison with the classical approach of "stereoscopic" video is compared.
Abstract: This paper presents details of a system that allows for an evolutionary introduction of depth perception into the existing 2D digital TV framework. The work is part of the European Information Society Technologies (IST) project “Advanced Three-Dimensional Television System Technologies” (ATTEST), an activity, where industries, research centers and universities have joined forces to design a backwards-compatible, flexible and modular broadcast 3D-TV system. At the very heart of the described new concept is the generation and distribution of a novel data representation format, which consists of monoscopic color video and associated perpixel depth information. From these data, one or more “virtual” views of a real-world scene can be synthesized in real-time at the receiver side (i. e. a 3D-TV set-top box) by means of so-called depth-image-based rendering (DIBR) techniques. This publication will provide: (1) a detailed description of the fundamentals of this new approach on 3D-TV; (2) a comparison with the classical approach of “stereoscopic” video; (3) a short introduction to DIBR techniques in general; (4) the development of a specific DIBR algorithm that can be used for the efficient generation of high-quality “virtual” stereoscopic views; (5) a number of implementation details that are specific to the current state of the development; (6) research on the backwards-compatible compression and transmission of 3D imagery using state-of-the-art MPEG (Moving Pictures Expert Group) tools.
1,560 citations
TL;DR: The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion, and there is a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities.
Abstract: Reversible watermarking enables the embedding of useful information in a host signal without any loss of host information. Tian's difference-expansion technique is a high-capacity, reversible method for data embedding. However, the method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. We propose a histogram shifting technique as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. We also propose a reversible data-embedding technique called prediction-error expansion. This new technique better exploits the correlation inherent in the neighborhood of a pixel than the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to form an effective method for data embedding. The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities
1,229 citations
TL;DR: Results indicate that the spatial, quad-based algorithm developed for color images allows for hiding the largest payload at the highest signal-to-noise ratio.
Abstract: A reversible watermarking algorithm with very high data-hiding capacity has been developed for color images. The algorithm allows the watermarking process to be reversed, which restores the exact original image. The algorithm hides several bits in the difference expansion of vectors of adjacent pixels. The required general reversible integer transform and the necessary conditions to avoid underflow and overflow are derived for any vector of arbitrary length. Also, the potential payload size that can be embedded into a host image is discussed, and a feedback system for controlling this size is developed. In addition, to maximize the amount of data that can be hidden into an image, the embedding algorithm can be applied recursively across the color components. Simulation results using spatial triplets, spatial quads, cross-color triplets, and cross-color quads are presented and compared with the existing reversible watermarking algorithms. These results indicate that the spatial, quad-based algorithm allows for hiding the largest payload at the highest signal-to-noise ratio.
1,149 citations