Image Evaluation Using Structural Similarity (SSIM) Index

This MATLAB program implements image quality evaluation using the Structural Similarity (SSIM) index criterion, which operates similarly to Peak Signal-to-Noise Ratio (PSNR) assessment methods. The SSIM algorithm specifically measures image similarity by accounting for three key components: luminance, contrast, and structural information. The implementation typically involves computing local statistics using sliding window processing, where mean, variance, and covariance calculations are performed on image patches. Key functions in this implementation may include: - Window-based local statistics computation using Gaussian weighting - Dynamic range normalization for luminance comparison - Stability constants to avoid division by zero in contrast and structure calculations - Multi-scale implementation options for enhanced accuracy By employing SSIM, we can perform more comprehensive image quality assessment that captures perceptual differences beyond simple error metrics like PSNR. This makes the program particularly valuable for understanding and evaluating image quality in applications such as image processing, compression optimization, and computer vision tasks where human perception matters. The function typically returns a quality map or single SSIM value between -1 and 1, where 1 indicates perfect similarity.