Image Quality Assessment: Mean Structural Similarity Index (MSSIM)

A crucial metric in image quality assessment is the Mean Structural Similarity Index (MSSIM). MSSIM demonstrates superior performance compared to Peak Signal-to-Noise Ratio (PSNR) while maintaining computational simplicity. As a widely adopted quality assessment measure in image processing, MSSIM evaluates structural similarity while considering luminance and contrast consistency between images. The algorithm typically involves partitioning images into local windows, computing SSIM values for each window (combining luminance, contrast, and structure comparisons), and averaging results across all windows. Key implementation steps include Gaussian weighting of windows and dynamic range normalization. By calculating structural similarity between images, MSSIM enables effective assessment of image quality and similarity levels. Consequently, in image quality evaluation, MSSIM serves as a vital indicator that facilitates better understanding and analysis of image quality characteristics through its perceptually relevant measurements.