Nearest Neighbor and Bilinear Interpolation Methods for Image Geometric Transformations (e.g. Rotation)

Two commonly used interpolation methods for image geometric transformations (such as rotation) are nearest neighbor interpolation and bilinear interpolation. The nearest neighbor interpolation method selects the value of the original pixel closest to the target pixel position as the interpolation result, making it computationally efficient but potentially resulting in jagged edges. In code implementation, this typically involves rounding the calculated coordinates to the nearest integer indices. Bilinear interpolation, on the other hand, calculates the interpolation result through a weighted average of four adjacent pixels, producing smoother results with better visual quality. This method requires computing two linear interpolations in the horizontal direction followed by one in the vertical direction (or vice-versa). The algorithm involves determining weights based on the relative distances between the target point and its four neighboring pixels, then applying these weights to calculate the final pixel value. This approach generally provides superior image quality compared to nearest neighbor interpolation, though it requires more computational resources. Both methods are fundamental in computer vision applications and are implemented in libraries like OpenCV through functions such as cv2.INTER_NEAREST and cv2.INTER_LINEAR when specifying interpolation parameters for geometric transformations.