Reversible Data Hiding Based on Structural Similarity Block Selection

Abstract

Reversible data hiding (RDH) methods are widely used in many privacy-sensitive real-time applications for digital images. As an efficient RDH method, prediction-error histogram (PEH) shifting technique has found wide application for its high efficiency in increasing embedding capacity. Nowadays, the performance of most PEH-based methods is evaluated only by Peak Signal to Noise Ratio (PSNR) or Structural Similarity (SSIM) Index. However, more and more applications require high quality in both PSNR and SSIM. Therefore, in this paper, we propose a new RDH method to concentrate on both PSNR and SSIM performance, which involves two key techniques: 1) an SSIM-based block selection technique, 2) a PEH-based optimal expansion bins selection technique. Through block selection, the original image is divided into two parts: smooth blocks and rough blocks. We select pixels of smooth block for double embedding and leave pixels of rough block unchanged. According to the optimal expansion bins, reasonable pixels are chosen for embedding such that the embedding distortion can be reduced. With these improvements, our proposed method has higher SSIM and PSNR after embedding compared to other PEH-based methods. The experimental results demonstrate its superiority over some state-of-the-art counterpart and other conventional PEH-based works.

Keywords

• Reversible data hiding
• prediction-error histogram shifting
• SSIM
• block selection
• double embedding