Parametric cubic convolution scaler for enlargement and reduction of image

An adaptive version of cubic convolution interpolation is derived for the enlargement or reduction of digital images by arbitrary scaling factors. In this paper, the problem of image interpolation is dealt with space-variant ones by introducing the adaptation of the interpolation kernel. The adaptation is performed in each subpixel which is predicted from the neighbor pixels. The interpolation kernel is modified to adapt local properties of the original data. During the adaptation phase, we modify the parameter value on which the interpolation is based to account for the concavity and continuity of data. Simulation results show that the proposed algorithm enable a reliable scaling technique with arbitrary scale factor. The algorithm exhibits significant improvement in the minimization of information loss when compared with the conventional interpolation algorithms.