Implementation of RGB Image Scaling using Bilinear Interpolation and Nearest Neighbor Interpolation

To achieve RGB image scaling, we can employ two primary methods: bilinear interpolation and nearest neighbor interpolation. Bilinear interpolation calculates new pixel values by performing weighted averaging between four adjacent known pixels, maintaining image smoothness during scaling operations. This method involves computing horizontal and vertical interpolations sequentially using the formula: f(x,y) ≈ [f(Q11)(x2-x)(y2-y) + f(Q21)(x-x1)(y2-y) + f(Q12)(x2-x)(y-y1) + f(Q22)(x-x1)(y-y1)] / [(x2-x1)(y2-y1)]. Nearest neighbor interpolation, conversely, assigns the value of the closest known pixel to the target pixel, preserving image sharpness by selecting the most proximate existing pixel value without averaging.

In MATLAB implementation, we can utilize built-in functions like imresize() with specified interpolation methods ('bilinear' or 'nearest'). For custom implementation, we would need to: 1) Separate RGB channels using imsplit() or manual indexing (:,:,1:3), 2) Calculate scaling ratios and new image dimensions, 3) Create coordinate mapping grids using meshgrid(), 4) Apply interpolation algorithms channel-wise through nested loops or vectorized operations, 5) Recombine channels using cat(3,R,G,B). Key functions involved include imread() for image loading, size() for dimension checking, and imshow() for result visualization. The algorithm handles edge cases by implementing boundary checks and optionally using symmetric padding via padarray().