目的 基于像素值排序（PVO）的数据隐藏算法因其高保真的优越性受到广泛重视，并不断得到改进。本文提出一种图像分区选择思想，以进一步充分利用图像的嵌入空间，改善PVO算法的嵌入性能，提高载秘图像的信噪比。方法 原始PVO算法通常采用预测差值“1”进行数据隐藏，对平滑像素组有较好的利用率和隐蔽性，而对毛躁像素组隐秘性能明显下降，算法性能与图像像素分布情况密切相关。本文在PVO算法基础上提出图像分区选择的思想，首先，将原始图像分为若干区域，然后按移位率从小到大的顺序依次选择图像区域；其次，在每个区域中选择合适的嵌入预测误差；最后，按顺序在被选区域利用该区域的最优嵌入差值完成信息嵌入。结果 假设将图像划分为8×8个区域，对本文算法与原始PVO算法进行比较，当嵌入量为1×10⁴ bit时，Elaine图像的移位率由81.59%降为74.40%，载秘图像的峰值信噪比（PSNR）值由55.388 2提高为56.996 9，提高了1.608 7，采用其他图像并就不同嵌入量进行实验，各图像PSNR值均表现出不同程度的提高。其次，将图像分别划分为2×2、4×4、8×8、16×16个分区，当嵌入量为1×10⁴ bit时，Lena图像PSNR由原始PVO的59.204 6逐渐增加至60.846 9，其他图像在不同嵌入量时PSNR均随着分区数的增加而有不同程度的提高。结论 本文提出的基于图像分区选择的改进PVO算法，可根据像素分布情况增加对嵌入空间的利用，在相同嵌入量情况下，改进后的算法能够获得更高的PSNR值；在一定分区数量条件范围内，分区数量与图像PSNR值表现出正相关性，随着分区数量的增加，图像PSNR值随之增加；本文方法在一定程度上改善了嵌入容量，弥补了因分区数量增加带来的辅助信息增加的问题。

Objective Reversible image data hiding refers to hiding information in an image. The information receiver not only can extract complete secret information correctly but also can restore the original image without loss. Reversible image data hiding is widely used in various fields, such as covert communication, medical image processing, copyright protection, and remote sensing technology. At present, many methods are used to implement image data hiding, including reversible data hiding method based on lossless compression, reversible data hiding method based on integer transform, reversible data hiding method based on histogram translation, and reversible data hiding method based on prediction error expansion. The reversible data hiding algorithm based on pixel value ordering (PVO) is widely regarded and improved constantly because of its high fidelity superiority. In this study, an improved PVO reversible data hiding algorithm based on the idea of image block selection is proposed to improve the embedding performance of PVO algorithm and improve the peak value signal-to-noise ratio (PSNR) of stego-image. Method The PVO method prosed by Li et al. is performed using the following procedures. First, the cover image is divided into non-overlapped groups. Second, the pixel values are sorted in an ascending order for each group. Third, the maximum pixel value is predicted by the sub-maximum pixel value to obtain the maximum prediction error for the pixel values after sorting in each group; the minimum pixel value is predicted by the sub-minimum pixel value to obtain the minimum prediction error. Finally, if prediction errors are equal to 1, then the pixels are used to carry the secret data. If prediction errors are greater than 1, then the pixels are shifted to create vacancy; otherwise, prediction errors are discarded in data embedding. The PVO algorithm usually uses the prediction errors equal to 1 for information hiding and thus exhibits good utilization and concealment capability for smooth image region. Otherwise, algorithm performance is obviously decreased and the performance of PVO algorithm is closely related to pixel distribution. Given that the distribution of pixels in different regions is non-uniform, if the image is divided equally into several regions, the embedding capacity used in the PVO algorithm in different regions differs. When the same amount of information is embedded in each partition, PSNR values also differ. Thus, this study proposes the idea of image block selection to fully utilize embedded space and improve image embedding performance. First, the original image is divided into several non-overlapping block areas, and the block area is selected on the basis of the order of the shift rate from small to large. Second, the appropriate embedding prediction error is selected in each block area. Finally, information embedding is carried out using the original PVO method on the basis of the block selection order and the optimal embedding difference of each block. Result First, images divided into 8×8 blocks are used in comparing the improved algorithm with the original PVO algorithm. When the embedding amount is 1×10⁴ bit, the shift rate of the Elaine image is reduced from 81.59% to 74.40%, the PSNR is increased from 55.388 2 to 56.996 9, the growth is 1.608 7, the PSNR value of the Aerial image is improved by 1.88, and the PSNR value of the Baboon image is improved by 2.29. The PSNR value of each image shows various degrees of improvement when the experiments are performed using different embedding amounts. Second, images divided into different number blocks, such as 2×2, 4×4, 8×8, or 16×16, are used in comparing the PSNR with the original PVO algorithm. The PSNR value of the Lena image is gradually increased from 59.204 6 to 60.846 9 when the embedding amount is 1×10⁴ bit, and the PSNR values of other images are increased with the increase in the number of blocks. Finally, the maximum embedding amount of the algorithm is counted in the case of different partitions. The maximum embedding amount of the original PVO algorithm is 14 972 bit, the maximum embedding is 14 992 bit when the image is divided into 4×4 blocks, and the maximum embedding is increased to 15 753 bit when the image is divided into 16×16 blocks. The maximum embedding of the image is improved with the increase in the number of blocks. Conclusion In this study, an improved PVO algorithm based on image block selection is proposed to increase the use of embedded space according to the distribution of pixels. The improved PVO algorithm can obtain high PSNR values and improve the visual experience of secret image in the same embedding amount by adopting image block selection and embedding prediction error optimization strategy in the process of information hiding. Within a certain number of blocks, the number of blocks shows positive correlation with the PSNR value of image. As the number of blocks increases, the PSNR value of image also increases. The method improves the embedding capacity to a certain extent and compensates for the increase in auxiliary information caused by the increase in the number of partitions. The algorithm exhibits a certain improvement in embedding capacity and image fidelity compared with the original PVO algorithm. Subsequent research will focus on the partition rules and optimization issues to further improve the performance of PVO-based algorithm.