<p>Most existing reversible data hiding (RDH) algorithms for color images adopt a fixed-ratio capacity allocation approach, often resulting in allocation errors. This paper presents a reversible data hiding scheme for color images, integrating adaptive capacity allocation (ACA) and pixel-value similarity distance (PVSD) sorting. In this paper, the adaptive capacity allocation method avoids pre-allocating capacity for the three RGB channels, instead combining them for unified data embedding. When the data embedding is completed, the capacity allocation can be realized. ACA effectively enhances pixel value correlation, aiding data embedding. Existing global pixel value ordering (PVO) methods utilize pixel complexity for secondary sorting. Pixel complexity, which describes local texture smoothness, does not accurately represent pixel value magnitude, leading to inaccurate secondary sorting. This paper employs pixel value similarity distance for secondary sorting. PVSD accurately assesses pixel value differences, enabling precise arrangement of similar-valued pixels in adjacent positions. It enhances pixel sequence smoothness, thereby improving the accuracy of the PVO prediction method. Experimental results demonstrate that the stego-images generated by the proposed scheme exhibit significantly superior visual quality compared to those from other state-of-the-art schemes. Specifically, an average PSNR of 61.38&#xa0;dB was achieved on the Kodak image dataset at a capacity of 20000 bits.</p>

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Color image reversible data hiding based on adaptive capacity allocation and pixel value similarity distance sorting

  • Ningxiong Mao,
  • Chunping Ouyang,
  • Yaping Wan,
  • Lingfeng Qu,
  • Yuan Yuan

摘要

Most existing reversible data hiding (RDH) algorithms for color images adopt a fixed-ratio capacity allocation approach, often resulting in allocation errors. This paper presents a reversible data hiding scheme for color images, integrating adaptive capacity allocation (ACA) and pixel-value similarity distance (PVSD) sorting. In this paper, the adaptive capacity allocation method avoids pre-allocating capacity for the three RGB channels, instead combining them for unified data embedding. When the data embedding is completed, the capacity allocation can be realized. ACA effectively enhances pixel value correlation, aiding data embedding. Existing global pixel value ordering (PVO) methods utilize pixel complexity for secondary sorting. Pixel complexity, which describes local texture smoothness, does not accurately represent pixel value magnitude, leading to inaccurate secondary sorting. This paper employs pixel value similarity distance for secondary sorting. PVSD accurately assesses pixel value differences, enabling precise arrangement of similar-valued pixels in adjacent positions. It enhances pixel sequence smoothness, thereby improving the accuracy of the PVO prediction method. Experimental results demonstrate that the stego-images generated by the proposed scheme exhibit significantly superior visual quality compared to those from other state-of-the-art schemes. Specifically, an average PSNR of 61.38 dB was achieved on the Kodak image dataset at a capacity of 20000 bits.