Reversible data hiding by adaptive modification of prediction errors

Abstract

Histogram Shifting (HS) is one of the most popular reversible data hiding techniques that has received tremendous attention from the research community in recent years. While histogram shifting offers many advantages, it suffers from relatively low payload, which restricts its applications significantly. In this work, a new reversible data hiding technique based on the modification of the histogram of prediction errors is proposed. The proposed method employs an adaptive strategy to vacate multiple bins as the embedding venues in order to increase the effective payload. The histogram bins are shifted dynamically based on their magnitudes. To maintain high quality for the output image, the distance of shifting is minimized for smaller prediction errors. On the other hand, the distance of shifting is allowed to be larger for larger prediction errors, which are of lower occurrences, to create more space for embedding. The proposed data hiding method is able to reversibly hide larger number of bits into the host image while achieving comparable output image quality when compared to the conventional histogram shifting based methods. The experimental results suggest that, on average, the proposed method is able to embed 0.247bpp into various standard test images, while still maintaining the visual quality at satisfactory level of ~48.9 dB.