Resize Image (Nearest Neighbor)

Implement an image resizing algorithm using the nearest neighbor interpolation technique. This task involves increasing or decreasing the dimensions of a given image while maintaining its visual content.

The concept of image resizing is fundamental in computer vision, as it enables images to be adapted for various applications, such as display on different devices or use in specific algorithms. Nearest neighbor interpolation is a simple, yet effective method for resizing images, which works by mapping each pixel in the output image to the nearest pixel in the input image. This process can be mathematically represented as finding the corresponding position in the input image for each output pixel position.

Here are the steps to achieve this:

Calculate the source x and y coordinates for each output pixel.
Use these coordinates to find the nearest source pixel. The calculation of source coordinates can be expressed as $src_x = \lfloor x \cdot \frac{W_{src}}{W_{dst}} \rfloor, \quad src_y = \lfloor y \cdot \frac{H_{src}}{H_{dst}} \rfloor$ .

src_x = \lfloor x \cdot \frac{W_{src}}{W_{dst}} \rfloor, \quad src_y = \lfloor y \cdot \frac{H_{src}}{H_{dst}} \rfloor

This technique is widely used in image processing applications.

Here are the steps to achieve this:

Calculate the source x and y coordinates for each output pixel.
Use these coordinates to find the nearest source pixel. The calculation of source coordinates can be expressed as $src_x = \lfloor x \cdot \frac{W_{src}}{W_{dst}} \rfloor, \quad src_y = \lfloor y \cdot \frac{H_{src}}{H_{dst}} \rfloor$ .

src_x = \lfloor x \cdot \frac{W_{src}}{W_{dst}} \rfloor, \quad src_y = \lfloor y \cdot \frac{H_{src}}{H_{dst}} \rfloor

This technique is widely used in image processing applications.

Example:

Constraints:

Test Results

Resize Image (Nearest Neighbor)

Example:

Constraints:

Test Results