Functions for Image Quality Assessment

This text introduces several functions for image quality assessment with detailed explanations of their significance and applications. In addition to the previously mentioned PSNR (Peak Signal-to-Noise Ratio), RMS (Root Mean Square Error), and NMSE (Normalized Mean Square Error), other commonly used image quality metrics include Structural Similarity Index (SSIM), PSNR-HVS (Peak Signal-to-Noise Ratio considering Human Visual System), and Perception-based Image Quality Evaluator (PIQE). These metrics can be programmed using pixel-wise comparison algorithms or structural similarity computations to assess the quality of denoised and compressed images. They find extensive applications in image processing and computer vision fields. By comprehensively implementing these evaluation functions through code, we can more accurately quantify image sharpness, detail loss, and distortion levels, thereby improving the performance and effectiveness of image processing algorithms and compression techniques.