We need metrics (values) to compare the results of models. These are a few well known metrics that are used for comparison. Do some research into each metric, understand the math, and implement a function that takes the required inputs and outputs the metric. We probably want the inputs to be NumPy arrays of the images we are examining. If you aren’t familiar with NumPy, see this page: NumPy: the absolute basics for beginners — NumPy v1.26 Manual Also be sure to check out the UTAT Python coding guidelines: Python Coding Guidelines - FINCH - Confluence (utat.ca)

Implement functions to calculate the following image quality metrics:

• PSNR
• SSIM
• LPIPS

Tasks:

• [ ] Understand the math behind the metrics (research/literature review)
• [ ] Create docstrings/pseudocode for each metric
• [ ] Create and upload a function to calculate each metric to the GitHub repository