High-fidelity reversible data hiding using adaptive context based pixel value ordering

Abstract

The preferred predictor for high-fidelity reversible data hiding (RDH), pixel value ordering, has attracted much attention in the past decade. Numerous studies demonstrate that the key to accurate prediction lies in a reasonably sized and full-enclosing context. Although this issue has been studied, the resulting construction methods are only applicable to RDH schemes that implement pixel-wise embedding. In this paper, a novel predictor based on adaptive context and applicable to RDH schemes that implement block-wise embedding is proposed. First, the cover image is divided into two independent block sets, based on which the target block can be combined with its nearest external pixels to form a new embedding unit. When predicting each pixel in the target block, consecutive pixels in the same row or column as the predicted pixel form the context. As the context is always full-enclosing, the proposed method effectively improves prediction quality. In addition, the context also enables the achievement of high-dimensional prediction-error expansion. Experimental results show that the proposed scheme can achieve satisfactory superiority in fidelity over several state-of-the-art RDH works.