Image Quality Assessment

The article discusses image quality assessment, which employs multiple metrics to evaluate image quality. Beyond the mentioned metrics such as entropy, average gradient, edge strength, and variance, other important indicators include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), image brightness, and contrast. These metrics effectively measure various aspects of image quality including sharpness, detail preservation, and contrast levels, making them widely applicable in image processing and computer vision fields. In practical implementations, entropy calculation typically involves computing the probability distribution of pixel intensities and applying the Shannon entropy formula. Average gradient can be implemented by calculating gradients using Sobel or Prewitt operators and averaging their magnitudes. Edge strength often utilizes edge detection algorithms like Canny or Sobel followed by intensity measurement. Variance computation involves determining the statistical dispersion of pixel values from the mean. PSNR implementation requires calculating Mean Squared Error (MSE) between original and processed images before applying the logarithmic formula. SSIM implementation typically involves comparing luminance, contrast, and structure components between reference and distorted images using sliding window approaches.