MATLAB Code Implementation for Image Sampling

Image sampling is a fundamental operation in digital image processing, which achieves data compression or accelerates processing by reducing image resolution. In MATLAB, implementing 128, 64, or 32-point sampling can be accomplished by adjusting the dimensions of the image matrix.

### Basic Approach Image Reading: Use `imread` to load the target image, obtaining the original pixel matrix. Sampling Rate Determination: Calculate the scaling ratio based on the target sampling points (e.g., 128×128). For example, if the original image is 512×512, 128-point sampling requires reducing both dimensions to 1/4 of the original. Downsampling Operation: Resize the image directly using `imresize`, or implement custom interval sampling (e.g., selecting every Nth row/column pixel). Display and Save: Compare pre- and post-sampling images with `imshow`, or save results using `imwrite`.

### Key Technical Considerations Anti-aliasing Treatment: Downsampling may introduce aliasing artifacts; the `'Antialiasing'` parameter in `imresize` optimizes smoothing effects. Interpolation Method Selection: Methods like bilinear interpolation (`'bilinear'`) balance processing speed and output quality.

### Extended Considerations Non-uniform Sampling: Preserve more sampling points for specific regions (e.g., edges). Dynamic Sampling: Adaptively adjust sampling rates based on image content complexity.

Note: Excessive sampling may lead to information loss—trade-offs between resolution and visual quality must be considered according to application requirements.