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. 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. First

images divided into 8×8 blocks are used in comparing the improved algorithm with the original PVO algorithm. When the embedding amount is 1×10 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 1×10 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. 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.