SSIM metric

[igv]

Use MAD (Mean Absolute Deviation) pooling, it's more accurate than mean pooling with SSIM metric (I, honestly, would trust PSNR more than SSIM with mean pooling). This is what I'm using for evaluation (using >2 scale levels doesn't make much of a difference):

import sys
from PIL import Image
import numpy as np
from scipy.ndimage import gaussian_filter

WEIGHTS = [0.0448]#, 0.2856, 0.3001, 0.2363, 0.1333]

def msssim(file1, file2):
    img1 = Image.open(file1).convert('RGB')
    img2 = Image.open(file2).convert('RGB')

    width, height = img1.size
    img1 = np.frombuffer(img1.tobytes(), dtype=np.uint8).reshape(height, width, 3) / 255
    img2 = np.frombuffer(img2.tobytes(), dtype=np.uint8).reshape(height, width, 3) / 255

    img1 = np.where(img1 > 0.04045, np.power((img1 + 0.055) / 1.055, 2.4),  img1 / 12.92)
    img2 = np.where(img2 > 0.04045, np.power((img2 + 0.055) / 1.055, 2.4),  img2 / 12.92)

    img1 = 0.2126 * img1[:,:,0] + 0.7152 * img1[:,:,1] + 0.0722 * img1[:,:,2]
    img2 = 0.2126 * img2[:,:,0] + 0.7152 * img2[:,:,1] + 0.0722 * img2[:,:,2]

    mssim = []
    for i in range(len(WEIGHTS)):
        mssim.append(ssim(pow(img1,1./2.2), pow(img2,1./2.2), i, i<len(WEIGHTS)-1))
        img1 = gaussian_filter(img1, 1.08, truncate=1.5)[::2,::2]
        img2 = gaussian_filter(img2, 1.08, truncate=1.5)[::2,::2]

    return np.sum(np.multiply(np.stack(mssim), WEIGHTS)) / np.sum(WEIGHTS)

def mad(x, l):
    return np.mean(np.absolute(x - np.power(np.mean(x), np.power(.5, l)))) # np.mean(np.absolute(x - np.mean(x if l==0 else np.sort(x, axis=None)[-int(x.size//1.5):])))

def ssim(L1, L2, lvl, cs_map):
    C1=(0.01)**2
    C2=(0.03)**2
    sd, t = 1.5, 3 #kernel radius = round(sd * truncate)

    mu1 = gaussian_filter(L1, sd, truncate=t)
    mu2 = gaussian_filter(L2, sd, truncate=t)
    mu1_sq = mu1 * mu1
    mu2_sq = mu2 * mu2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = gaussian_filter(L1 * L1, sd, truncate=t) - mu1_sq
    sigma2_sq = gaussian_filter(L2 * L2, sd, truncate=t) - mu2_sq
    sigma12 = gaussian_filter(L1 * L2, sd, truncate=t) - mu1_mu2

    if cs_map:
        value = (2.0*sigma12 + C2)/(sigma1_sq + sigma2_sq + C2)
    else:
        value = ((2.0*mu1_mu2 + C1)*(2.0*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*
                    (sigma1_sq + sigma2_sq + C2))

    return mad(value, lvl)

def main():
    for arg in sys.argv[2:]:
        score = msssim(sys.argv[1], arg)
        print(str(score) + "\t" + arg)

if __name__ == '__main__':
    main()

[Alexkral]

I use SSIM with mean pooling (and PSNR with peak value = 1) because it is the way these methods are normally used, so the results can be compared to any other. But I'll take a look at SSIM with MAD pooling for my personal reference.

On the other hand I wonder if it could be useful as a loss function.

[igv]

Yes, SSIM normally used with mean pooling and it's usually useless, because it's wrong.

I wonder if it could be useful as a loss function.

You will probably have to clamp covariance to range [0; 1].