Project Title: Comparing the Performance of Different Image Interpolation Techniques on a Data Set of Images

Objective: The objective of this project is to compare the performance of different image interpolation techniques on a data set of images. Image interpolation is a common image processing technique used to increase or decrease the size of an image. There are several different interpolation techniques available, such as nearest neighbor, bilinear, and bicubic interpolation. The goal of this project is to compare the accuracy, speed, and visual quality of these different interpolation techniques on a data set of images.

Methodology:

1.Collect a data set of images of various sizes and resolutions.

2.Implement the nearest neighbor, bilinear, and bicubic interpolation techniques in a programming language of your choice (such as Python).

3.Write a program that resizes each image in the data set using each of the interpolation techniques.

4.Measure the accuracy of the interpolated images using a metric such as the mean squared error (MSE) between the original and interpolated images.

5.Measure the speed of each interpolation technique by timing how long it takes to interpolate each image.

6.Compare the accuracy, speed, and visual quality of the interpolated images for each technique.

Expected Results:

By comparing the performance of different image interpolation techniques on a data set of images, you should be able to identify which techniques are most accurate, fast, and visually pleasing for a given set of images. This information can be useful for image processing applications that require image resizing, such as image analysis or computer vision tasks.

Potential Extensions:

You could extend this project by comparing additional interpolation techniques, such as Lanczos or spline interpolation.

You could also compare the performance of different interpolation techniques on different types of images (such as photographs, line drawings, or text) to see if one technique performs better on certain types of images.

You could also compare the performance of different interpolation techniques on images with different levels of noise or compression to see how these factors affect the accuracy and quality of the interpolated images.

I need the python code which is the answer to what is written above. Thanks in advance.