Reversible Embedding to Covers Full of Boundaries

TL;DR

This paper introduces a new reversible data embedding framework that efficiently handles images with many boundary pixels by losslessly preprocessing boundary pixels to reduce side information, enhancing embedding capacity.

Contribution

The paper proposes a novel preprocessing method that significantly reduces side information in reversible data embedding for images with numerous boundary pixels.

Findings

Reduced side information size in boundary-rich images

Enhanced pure embedding capacity

Demonstrated effectiveness through experiments

Abstract

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.