Modified cubic convolution scaler with minimum loss of information

The authors derive an adaptive version of cubic convolution interpolation for the enlargement or reduction of digital images by arbi- trary scaling factors. The adaptation is performed in each subblock (typi- cally L3L rectangular) of an image. It consists of three phases: two scaling procedures (i.e., forward and backward interpolation) and an op- timization of the interpolation kernel. In the forward interpolation phase, from the sampled data with the original resolution, we generate scaled data with different (higher or lower) resolution. The backward interpola- tion produces new discrete data by applying another interpolation to the scaled one. The phases are based on a cubic convolution interpolation whose kernel is modified to adapt to local properties of the data. During the optimization phase, we modify the parameter values to decrease the disparity between the original data and those resulting from another in- terpolation on the different-resolution output of the forward interpolating phase. The overall process is repeated iteratively. We show experimen- tal results that demonstrate the effectiveness of the proposed interpola- tion method. The algorithm exhibits significant improvement in the mini- mization of information loss when compared with the conventional interpolation algorithms.