MATLAB Code for Image Quality Assessment Based on Structural Similarity

This article presents a MATLAB implementation for image quality assessment based on the Structural Similarity Index (SSIM). The code provides a robust framework for evaluating image quality by analyzing structural similarities between reference and distorted images. The implementation follows the standard SSIM algorithm which computes luminance, contrast, and structure comparisons between image patches using sliding window approach. Key features of this MATLAB code include: - Implementation of SSIM metric with configurable window size and parameters - Gaussian weighting function for local window analysis - Multi-scale SSIM capability for comprehensive quality assessment - Detailed comments explaining each computational step and parameter significance The code structure is organized into main functions including: 1. ssim() - Main function calculating SSIM index between two images 2. gaussian_window() - Generates Gaussian weighting matrix 3. ssim_map() - Produces local SSIM map for detailed regional analysis To utilize this code, users need MATLAB installed on their system. The implementation includes thorough documentation and example usage scenarios to facilitate understanding of image quality assessment principles. This tool enables quantitative comparison of image degradation types and helps researchers make accurate quality judgments for various image processing applications. The code's modular design allows easy integration with existing MATLAB image processing workflows and can be extended for specialized quality assessment requirements. Through this implementation, users can gain deeper insights into image characteristics and perform more precise quality evaluations in computer vision and image analysis projects.