peak signal to noise ratio Algorithm
Peak signal-to-noise ratio, often abbreviated PSNR, is an technology term for the ratio between the maximal possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide dynamic range, PSNR is normally expressed in terms of the logarithmic decibel scale.
"""
Peak signal-to-noise ratio - PSNR - https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source: https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python/
"""
import math
import os
import cv2
import numpy as np
def psnr(original, contrast):
mse = np.mean((original - contrast) ** 2)
if mse == 0:
return 100
PIXEL_MAX = 255.0
PSNR = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
return PSNR
def main():
dir_path = os.path.dirname(os.path.realpath(__file__))
# Loading images (original image and compressed image)
original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)
original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
contrast2 = cv2.imread(
os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
)
# Value expected: 29.73dB
print("-- First Test --")
print(f"PSNR value is {psnr(original, contrast)} dB")
# # Value expected: 31.53dB (Wikipedia Example)
print("\n-- Second Test --")
print(f"PSNR value is {psnr(original2, contrast2)} dB")
if __name__ == "__main__":
main()
