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How do I calculate the maximum signal to noise ratio (PSNR) in Python? Is there any library that can calculate PSNR for Image?

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You can use cv2.PSNR like this example:


import cv2
img1 = cv2.imread('img1.bmp')
img2 = cv2.imread('img2.bmp')
psnr = cv2.PSNR(img1, img2)
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The steps for calculation of PSNR value of two images:

import math
import cv2
import numpy as np

original = cv2.imread("original.png")
contrast = cv2.imread("photoshopped.png", 1)

def psnr(img1, img2):
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return 100
    PIXEL_MAX = 255.0
    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))

d = psnr(original, contrast)
print(d)

Details.

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    $\begingroup$ Why return 100 if the MSE == 0? $\endgroup$
    – Coda Chang
    Apr 1 '19 at 6:20
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turn to float first!!!!!!!! turn to float first!!!!!!!! turn to float first!!!!!!!!

def compute_psnr(img1, img2):
img1 = img1.astype(np.float64) / 255.
img2 = img2.astype(np.float64) / 255.
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
    return "Same Image"
return 10 * math.log10(1. / mse)
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  • $\begingroup$ Technically, you don't need to convert to float specifically. That is done automatically: np.mean(np.arange(3)**2) == 1.666.... However, if (img1 - img2) is of dtype np.uint8, you will of course not be able to store the results of the computation in an 8-bit unsigned integer. $\endgroup$ Aug 11 at 8:34
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If you have a replica of your signal (image) that is noise free, you can calculate the correlation coefficient which is directly related to SNR.

See my response here for specific details on determining the correlation coefficient and from that SNR:

Noise detection

In this context there is no "maximum SNR" but will be the SNR for your entire image, meaning the power of your desired signal relative to everything else (distortions).

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  • $\begingroup$ thanks for your reply. I posted that is there have any lib in python that can calculate PSNR?? $\endgroup$
    – Sudip Das
    Mar 2 '17 at 19:26
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    $\begingroup$ I do not know of one that makes it any simpler than that- what I described is what I do in Python, and the approach is very straightforward. $\endgroup$ Mar 2 '17 at 19:29

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