Understanding image error metrics, mean squared error

1. What is MSE of an image?
2. How is MSE calculated for images?
3. How do you explain mean squared error?
4. How does Matlab calculate MSE of an image?

What is MSE of an image?

The mean-square error (MSE) and the peak signal-to-noise ratio (PSNR) are used to compare image compression quality. The MSE represents the cumulative squared error between the compressed and the original image, whereas PSNR represents a measure of the peak error.

How is MSE calculated for images?

For two pictures A, B you take the square of the difference between every pixel in A and the corresponding pixel in B, sum that up and divide it by the number of pixels.

How do you explain mean squared error?

The Mean Squared Error measures how close a regression line is to a set of data points. It is a risk function corresponding to the expected value of the squared error loss. Mean square error is calculated by taking the average, specifically the mean, of errors squared from data as it relates to a function.

How does Matlab calculate MSE of an image?

err = immse( X , Y ) calculates the mean-squared error (MSE) between the arrays X and Y . A lower MSE value indicates greater similarity between X and Y .