A tool used to estimate the Mean Squared Error (MSE) between two images aids in quantifying the perceptual difference between them. This metric is frequently employed in image processing, particularly for evaluating image compression algorithms and other image manipulation techniques. For example, comparing an original image to a compressed version allows one to measure the information lost during compression.
This quantitative assessment of image quality is vital for optimizing algorithms and ensuring visual fidelity. By minimizing the MSE, developers can strive for perceptually similar output images after processing. Historically, MSE has been a cornerstone in image quality assessment due to its computational simplicity and mathematical interpretability. Its widespread adoption across diverse fields, including medical imaging, remote sensing, and computer vision, underscores its significance.