Pixel-value-ordering (PVO) is a widely used reversible data hiding (RDH) framework which aims to achieve the high quality of stego-image under low capacity. In this letter, we propose a general location-based adaptive complexity for PVO. Different from the block-based complexity in the previous PVO-based methods, our proposed method adaptively selects context pixels from the perspective of the relative locations of predicted pixel and prediction pixel. Consequently, different number of high correlation context pixels can be adaptively selected and the context pixels can break the limitation of the current block. Moreover, instead of sharing the same block complexity by two predicted pixels in the current block, each predicted pixel can be utilized independently according to its own corresponding complexity. Our proposed adaptive complexity can combine with any PVO-based methods and the experimental results show that our proposed method achieves a significant improvement in prediction accuracy and embedding performance.

中文翻译：

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基于像素值排序的可逆数据隐藏的自适应复杂度


像素值排序（PVO）是一种广泛使用的可逆数据隐藏（RDH）框架，旨在在低容量下实现高质量的隐写图像。在这封信中，我们提出了一种基于位置的通用 PVO 自适应复杂度。与之前基于 PVO 的方法中基于块的复杂性不同，我们提出的方法从预测像素和预测像素的相对位置的角度自适应地选择上下文像素。因此，可以自适应地选择不同数量的高相关上下文像素，并且上下文像素可以打破当前块的限制。此外，不是由当前块中的两个预测像素共享相同的块复杂度，而是可以根据其自身对应的复杂度来独立地利用每个预测像素。我们提出的自适应复杂度可以与任何基于 PVO 的方法结合，实验结果表明我们提出的方法在预测精度和嵌入性能方面取得了显着的提高。