Creating a noise model with a fixed PSNR

1. What is a good PSNR value in image processing?
2. Why PSNR instead of MSE?
3. How is PSNR calculated?
4. How do I increase the PSNR of an image?

What is a good PSNR value in image processing?

Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, provided the bit depth is 8 bits, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher. For 16-bit data typical values for the PSNR are between 60 and 80 dB.

Why PSNR instead of MSE?

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.

How is PSNR calculated?

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 do I increase the PSNR of an image?

PSNR is just a measure of quality of an processed image form original image. To increase PSNR of an image, you should first remove noice from the image using some filters, refer noise removal for more information. Type of filter will depend on the type of noise in the image.