What is mean square error in signals?

Mean Square Error (MSE) in signals is a measure of the average squared differences between the estimated or predicted values and the actual values. It quantifies the accuracy of a signal processing model by calculating the mean of the squares of these errors, providing a scalar value that reflects the extent of error. A lower MSE indicates better model performance, as it signifies that the predicted values are closer to the actual values. MSE is widely used in various applications, including signal reconstruction and estimation.