  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.
