Image Quality Assessment Metrics: Signal-to-Noise Ratio and Beyond

In image processing, quality assessment metrics are critically important for evaluating results. Beyond common metrics like Signal-to-Noise Ratio (SNR), which can be calculated using MATLAB's snr() function or through custom implementations comparing signal variance to noise variance, there are several other significant quality indicators. Image sharpness measures the clarity of details in an image, often evaluated using algorithms like gradient magnitude calculations or Laplacian variance methods. Image contrast represents the difference in luminance between different regions, typically computed using statistical measures like standard deviation or Michelson contrast formulas. Additionally, metrics such as color saturation (assessing color intensity distribution) and edge sharpness (measured using edge detection algorithms like Canny or Sobel operators) provide comprehensive quality evaluation. Therefore, when processing images, it's essential to comprehensively consider multiple metrics through integrated assessment frameworks or weighted scoring systems to accurately evaluate overall image quality.