Several Image Quality Assessment Functions

This article introduces several commonly used functions for image quality assessment. These include PSNR (Peak Signal-to-Noise Ratio), RMS (Root Mean Square Error), and NMSE (Normalized Mean Square Error). These functions can be effectively applied to evaluate the quality of denoised and compressed images. For denoised images, PSNR serves as a crucial metric to assess image clarity and noise levels by calculating the ratio between the maximum possible signal power and the corrupting noise power. RMS error quantitatively measures the pixel-wise difference between processed and original images, where smaller values indicate closer resemblance to the reference image. NMSE provides a comprehensive evaluation that considers both image differences and dimensional scaling through normalization, making it particularly valuable for comparing images of varying sizes. Implementation-wise, these functions typically involve: - PSNR: Calculating mean squared error followed by logarithmic conversion using MAX²/MSE formula - RMS: Computing the square root of averaged squared differences across all pixels - NMSE: Normalizing the mean squared error by the variance or energy of the reference image Thus, these assessment functions play vital roles in image processing workflows, enabling precise quantitative evaluation of image quality through standardized computational approaches.