Image Processing Performance Evaluation

Image processing performance evaluation is a method for quantitatively assessing the quality of image processing results. Key evaluation metrics include Peak Signal-to-Noise Ratio (PSNR), Entropy, and Mean Square Error (MSE). By computing these metrics, researchers can objectively evaluate the performance of image processing algorithms. In code implementations, PSNR calculation typically involves comparing the original and processed images using logarithmic scale measurements of signal strength relative to noise. Entropy calculation measures the information content and complexity of an image through probability distribution analysis of pixel values. MSE computation involves averaging the squared differences between corresponding pixels in original and processed images. These quantitative assessments play a significant role in computer vision and image processing fields, helping researchers and engineers better understand and improve image processing algorithms through standardized metric comparisons.