A Novel Multilevel DCT Based Reversible Data Hiding

2010 ◽  
pp. 204-233
Author(s):  
Hong Cai ◽  
Sos S. Agaian
Keyword(s):  
Image Processing ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Decomposition Method ◽  
Hilbert Curve ◽  
High Quality ◽  
Distinct Frequency ◽  
Dct Coefficients ◽  
Lossless Data Hiding ◽  
Frequency Properties

DCT and wavelet based techniques have been widely used in image processing, for example, the applications involving JPEG, MPEG and JPEG2000. To combine the advantages of DCT and wavelet, we introduce in this chapter a novel multilevel DCT decomposition method by exploiting the modified inverse Hilbert curve. The experimental results showed that the proposed multilevel decomposition can extract characteristic DCT coefficients and assign the coefficients to new neighborhoods with distinct frequency properties. We discuss a powerful reversible data hiding algorithm in JPEG images based on this new multilevel DCT. This lossless data hiding algorithm features a key-dependent (multilevel structure) coefficient-extension technique and an embedding location selector, and it can achieve high quality reconstructed images with disparate content types.

Steganographic data hiding in image processing using predictive differencing

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
C. Weng ◽  
H. Tso ◽  
S. Wang
Keyword(s):  
Image Processing ◽  
Data Hiding ◽  
Predictive Value ◽  
Experimental Result ◽  
Embedding Capacity ◽  
High Quality ◽  
Pixel Value ◽  
Pixel Value Differencing

AbstractIn this paper, we propose a stenography scheme based on predictive differencing to embed data in a grey-image. In order to promote the embedding capacity of pixel-value differencing (PVD), we use differencing between a predictive value and an input pixel as the predictive differencing to embed the message where a predictive value is calculated by using various predictors. If the predictive differencing is large, then it means that the input pixel is located in the edge area and, thus, has a larger embedding capacity than the pixel in a smooth area. The experimental result shows that our proposed scheme is capable of providing greater embedding capacity and high quality of stego-images then previous works. Furthermore, we have also applied various predictors to evaluate our proposed scheme.

Reversible image processing via reversible data hiding

2016 ◽  
Author(s):  
Dongdong Hou ◽  
Weiming Zhang ◽  
Zihao Zhan ◽  
Ruiqi Jiang ◽  
Yang Yang ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Hiding ◽  
Reversible Data Hiding

A novel embedding technique for lossless data hiding in medical images employing histogram shifting method

2014 ◽  
Vol 12 (03) ◽  
pp. 1450026
Author(s):  
J. A. Alex Rajju Balan ◽  
S. Edward Rajan
Keyword(s):  
Data Hiding ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Zero Point ◽  
Discrete Wavelet ◽  
Histogram Shifting ◽  
Histogram Modification ◽  
Modification Technique ◽  
Dct Coefficients ◽  
Lossless Data Hiding

In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.

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