Image Sampling Method Based on Generalized Ricci Curvature and Depth Information

The improvement of sampling technology and the dramatic increasement of data have affected the storage and transmission of data, thus resampling is an important issue to compress the big amount of data. In this paper, we peer into the sampling techniques of gray scale images. Ricci curvature in density manifolds is introduced to obtain the measurement of sampling points. First of all, images are regarded as the color-patch map of two-dimensional manifolds. The auxiliary or constructed depth information is filtered to make the object areas kept for resampling. Then, both depth and gray information are leveraged to get the Ricci curvature in density manifolds. Finally, the candidate sampling points are selected and the image is reconstructed based on the measurement relationship. In addition, in order to balance the speed and precision of the reconstruction, both global sampling and local sampling are proposed. A large number of standard test images are used in the experiments, and the results show that the proposed method can be used to compress gray image effectively. Compared with the state-of-the-arts, the proposed method has more sampling points distributed on the edge of the object area where the gray value changes dramatically, while the sampling points are relatively less in the smooth area.