1. Field of Invention
The present invention relates to a digital image interpolation method, and more particularly to a digital image interpolation method implementing Gauss weight.
2. Related Art
When an image display has to be magnified/reduced or rotated, the original image pixels are typically rearranged according to a new configuration of pixels. If an image is magnified, the holes between the original pixels also are magnified, and new pixels must be used to fill these holes. When the image is reduced, the pixels are mixed with one another to form new pixels. To fill a created hole, a method known in the art performs a calculation based upon the color values of neighboring points to obtain a color value to be filled in the hole. This method is generally called an interpolation method, generally implemented in the rearrangement of image pixels. The purpose of the interpolation method is to determine the filling data of holes between known image pixels.
Several digital image interpolation methods are known in the art, the fastest method consists of copying the color values of the nearest neighboring pixel points. Although this latter method is faster, it usually creates saw tooth effects. Another method is the bilinear interpolation, in which the weight values of the four adjacently neighboring pixel points of each considered pixel are taken, and adequate shadows between the pixels are established to obtain a smooth transition. Although the bilinear interpolation provides better results, it is more time-consuming. The bi-cubic interpolation is another known method. The method of bilinear interpolation employs a four-pixel neighborhood surrounding the calculated pixel address to obtain a new transformed pixel value. Similarly, the method of bi-cubic interpolation involves fitting a series of cubic polynomials to the brightness values contained in the 4×4 array of pixels surrounding the calculated address. The bi-cubic interpolation method provides the best results, but is even more time-consuming than the bilinear interpolation. Therefore, the interpolation methods of the prior arts as described above still have disadvantages, either providing unsatisfactory results or being excessively time-consuming. As a result, there is a need for an interpolation method that, implemented in the process of image transformation, is less time-consuming.