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Why does one use MSE as a measure of quality. What is the scientific/mathematical reason for the same?
Mean-Square error is an error metric for measuring image or video quality it is popular video and image quality metric because the analysis and mathematics is easier with this L2-Norm metric. Most video and image quality experts will agree that MSE is not a very good measure of perceptual video and image quality.
The mathematical reasoning behind the MSE is as follows: For any real applications, noise in the readings or the labels is inevitable. We generally assume this noise follows Gaussian distribution and this holds perfectly well for most of the real applications. Considering 'e' follows gaussian distribution in y=f(x) + e and calculating the MLE, we get MSE which is also L2 distance. Note: Assuming some other noise distribution may lead to other MLE estimate which will not be MSE.
MSE is used for understanding the weight of the errors in any model. This helps us understand model accuracy in a way that is helpful when choosing different types of models. Check out more answers on InterviewQuery.com
(1) Back-propagation; (2) overfit vs underfit; (3) implementation of one of the steps in Canny edge detection using graphs; (4) closed form formula to solution of linear regression; (5) write code for K nearest neighbor algorithm (6) vanishing gradient vs exploding gradients due to choice to relu() vs sigmoid()
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