Reversible Image Data Hiding with Local Adaptive Contrast Enhancement
Recently, a novel reversible data hiding scheme is proposed for contrast enhancement by Wu (IEEE Signal Process Lett 22.1:81–85, 2015 ). Instead of pursuing the traditional high PSNR value, he designs the message embedding algorithm to enhance the contras
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Abstract Recently, a novel reversible data hiding scheme is proposed for contrast enhancement by Wu (IEEE Signal Process Lett 22.1:81–85, 2015). Instead of pursuing the traditional high PSNR value, he designs the message embedding algorithm to enhance the contrast of the host image. In this paper, an extended scheme is proposed to not only adaptively enhance the contrast of the image, but also to keep the PSNR value high meanwhile. Firstly, the original host image is divided into non-overlapping blocks, such that the local contrast of the image can be enhanced adaptively. Secondly, we classify the pixels of each block into two sets, the “referenced” set and the “embedded” set, and then processing them alternatively such that additional side information is eliminated. Experimental results demonstrate that our proposed algorithm achieves increased local visual quality and performs better than Wu et al.’s scheme with keeping image’s PSNR high as criterion for RDH. Keywords Local adaptive Reversible data hiding
Contrast enhancement Histogram modification
1 Introduction Data hiding is applied extensively in the community of signal processing, such as ownership protection, fingerprinting, authentication and secret communication. The most classical data hiding technique leads to permanent distortions. In last decades, reversible data hiding (RDH) [1–11] as a new type of information hiding technique, has received much attention. RDH provides not only extracting embedded data precisely, but also restoring the original cover image without any error. This special R. Jiang W. Zhang (&) J. Xu N. Yu X. Hu Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei, China e-mail: [email protected] © Springer Science+Business Media Singapore 2016 J.J. (Jong Hyuk) Park et al. (eds.), Advanced Multimedia and Ubiquitous Engineering, Lecture Notes in Electrical Engineering 393, DOI 10.1007/978-981-10-1536-6_58
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property has been found much useful in many sensitive fields, such as medical imagery, military imagery and law forensics, where any change on the host signal is not allowed. The classic RDH algorithms mainly adopt three techniques, histogram shifting (HS) [3], difference expansion (DE) [4] and prediction-error expansion (PEE) [5]. There are two important metrics to evaluate the performance of a RDH algorithm: the embedding capacity and the distortion. In fact, we always keep the balance between this two metrics, because a higher embedding capacity often cause more distortion and decrease the distortion will result in a lower embedding capacity, so we usually use the Peak-Signal-to-Noise-Ratio (PSNR) to evaluate the performance of algorithm. Recently, Wu et al. proposed a new RDH algorithm with contrast enhancement [1]. They deemed that the improvement of visual quality is more important than keeping the image’s PSNR high. By using some pairs of peaks, the histogram of pixels values is modified to achieve histogram equalization, thus leading to the image cont
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