What is the difference between MSE and PSNR of images?

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. The lower the value of MSE, the lower the error.

1. Why is PSNR better than MSE?
2. What is PSNR value of an image?
3. How is PSNR and MSE calculated?
4. What is the difference between PSNR and SSIM?

Why is PSNR better than MSE?

Thus, the advantages of the PSNR over the MSE are: (1) it enables to compare results on images encoded with a different number of bits, (2) concision. However, by definition, PSNR is nothing more than a normalized version of the MSE.

What is PSNR value of an image?

Peak signal-to-noise ratio (PSNR) is the ratio between the maximum possible power of an image and the power of corrupting noise that affects the quality of its representation. To estimate the PSNR of an image, it is necessary to compare that image to an ideal clean image with the maximum possible power.

How is PSNR and MSE calculated?

We then have PSNR = 10 log10(MAX^2/MSE) = 10 log10(MAX/(MSE)^(1/2))^2 = 20 log10(MAX/(MSE)^(1/2)). Therefore, PSNR = 20 log10(MAX/(MSE)^(1/2)).

What is the difference between PSNR and SSIM?

PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system.