Classic SSIM Implementation for Planar Image Quality Assessment

This is the most classic implementation of SSIM (Structural Similarity Index) for planar image quality assessment, characterized by its simple yet efficient algorithmic approach. SSIM is a metric used to measure the similarity between two images, taking into account three key aspects: luminance, contrast, and structural differences. The algorithm implementation is remarkably straightforward - it primarily involves comparing pixel values between images and calculating separate components for luminance (using mean values), contrast (using standard deviations), and structure (using covariance). These components are then combined using a weighted formula to produce a similarity score ranging from -1 to 1, where 1 indicates perfect similarity. In code implementation, the algorithm typically involves dividing images into local windows, computing statistics for each window, and then aggregating the results. Key functions would include Gaussian weighting for window processing, mean and variance calculations, and the final SSIM index computation. This algorithm is widely used in image processing and computer vision fields and holds significant importance for objective image quality assessment, particularly in applications like compression evaluation, restoration quality measurement, and benchmarking image processing algorithms.