2,547 research outputs found
Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding
This paper presents a novel reversible data hiding (RDH) algorithm for
gray-scaled images, in which the prediction-error of prediction error (PPE) of
a pixel is used to carry the secret data. In the proposed method, the pixels to
be embedded are firstly predicted with their neighboring pixels to obtain the
corresponding prediction errors (PEs). Then, by exploiting the PEs of the
neighboring pixels, the prediction of the PEs of the pixels can be determined.
And, a sorting technique based on the local complexity of a pixel is used to
collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will
be processed first for data embedding. By reversibly shifting the PPE histogram
(PPEH) with optimized parameters, the pixels corresponding to the altered PPEH
bins can be finally modified to carry the secret data. Experimental results
have implied that the proposed method can benefit from the prediction procedure
of the PEs, sorting technique as well as parameters selection, and therefore
outperform some state-of-the-art works in terms of payload-distortion
performance when applied to different images.Comment: There has no technical difference to previous versions, but rather
some minor word corrections. A 2-page summary of this paper was accepted by
ACM IH&MMSec'16 "Ongoing work session". My homepage: hzwu.github.i
Reversible Embedding to Covers Full of Boundaries
In reversible data embedding, to avoid overflow and underflow problem, before
data embedding, boundary pixels are recorded as side information, which may be
losslessly compressed. The existing algorithms often assume that a natural
image has little boundary pixels so that the size of side information is small.
Accordingly, a relatively high pure payload could be achieved. However, there
actually may exist a lot of boundary pixels in a natural image, implying that,
the size of side information could be very large. Therefore, when to directly
use the existing algorithms, the pure embedding capacity may be not sufficient.
In order to address this problem, in this paper, we present a new and efficient
framework to reversible data embedding in images that have lots of boundary
pixels. The core idea is to losslessly preprocess boundary pixels so that it
can significantly reduce the side information. Experimental results have shown
the superiority and applicability of our work
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