MATLAB Implementation of Bilinear Interpolation

Bilinear interpolation is a widely used image processing technique, particularly suitable for applications requiring reconstruction of continuous images from discrete pixel points. When implementing bilinear interpolation in MATLAB, the algorithm typically considers four nearest neighboring pixels surrounding the target position and calculates the new pixel value through weighted averaging. The MATLAB implementation involves using meshgrid functions to create coordinate matrices and performing vectorized operations for efficient computation.

In the context of motion compensation, bilinear interpolation helps process non-integer displacements obtained after motion estimation. When motion vectors point to positions between two pixels, directly using the nearest pixel value would cause blocking artifacts, while bilinear interpolation provides smoother transitions. This is particularly useful in video processing algorithms where interp2 or similar functions can be applied to motion-compensated frames.

The core approach of MATLAB implementation involves first determining the target point's position in the original image, which typically has non-integer coordinates. The algorithm then identifies four surrounding pixels that enclose this point. Based on the relative distances between the target point and these four pixels, appropriate weights are calculated. Finally, a weighted sum of the four original pixel values is computed to obtain the interpolated new pixel value. Key MATLAB functions for implementation may include coordinate normalization, weight calculation using fractional parts, and matrix operations for efficient pixel value computation.

Compared to nearest-neighbor interpolation, this method provides higher accuracy at the cost of slightly increased computational complexity. It effectively maintains image details while reducing aliasing effects, making it widely adopted in video coding and image reconstruction applications. It's important to note that bilinear interpolation may cause slight image blurring, which is an inevitable side effect of the smoothing process. The implementation should balance between interpolation quality and computational efficiency based on application requirements.