MATLAB Code Implementation for Image Downsampling

To achieve image downsampling, various sampling methods can be employed through MATLAB implementations. The nearest-neighbor interpolation method, commonly implemented using MATLAB's imresize function with the 'nearest' parameter, selects the closest original pixel to the target sampling location. This method is computationally efficient but may result in aliasing artifacts. The bilinear interpolation method, implemented with the 'bilinear' parameter in imresize, calculates new pixel values by computing weighted averages of differences between adjacent pixels in both horizontal and vertical directions. This approach provides smoother results than nearest-neighbor interpolation by considering four surrounding pixels. Another widely used technique is bicubic convolution, implemented using the 'bicubic' parameter, which generates new pixel values by calculating contributions from 16 surrounding pixels using cubic polynomials. This method typically produces the highest quality results among the three, preserving finer image details while being more computationally intensive. By implementing these different sampling methods in MATLAB, developers can effectively achieve image downsampling while maintaining critical image details. Each method offers distinct trade-offs between computational efficiency and output quality, allowing selection based on specific application requirements. The core implementation involves using MATLAB's image processing toolbox functions, particularly imresize with appropriate interpolation parameters, combined with proper handling of image matrices and coordinate transformations.