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I want to compare the similarity of two images by looking at its structure and colors. I need a measure that takes both structural and color fidelity into account. When I checked the formulation of original paper, it is said only x and y mean and variance is measured. Does SSIM account for structure only or does it take color similarity into account too?

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What usually can be done is calculate it per channel and have the average (Or weighted average).

So if you have $ {R}_{1}, {G}_{1}, {B}_{1} $ and $ {R}_{2}, {G}_{2}, {B}_{2} $ you would do:

$$ {SSIM}_{r} \left( {R}_{1}, {R}_{2} \right), {SSIM}_{g} \left( {G}_{1}, {G}_{2} \right), {SSIM}_{b} \left( {B}_{1}, {B}_{2} \right) $$

Then $ {SSIM}_{rgb} = {w}_{r} {SSIM}_{r} + {w}_{g} {SSIM}_{g} + {w}_{b} {SSIM}_{b} $.

For instance, MATLAB's ssim() does the above with $ {w}_{r} = {w}_{g} = {w}_{b} = \frac{1}{3} $.

SciKit Image's compare_ssim() also apply it channel wise using the multichannel flag:

enter image description here

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  • $\begingroup$ I just made a 512*512 all orange jpeg and the same size yellow jpeg file. Then I measured the similarity by using SSIM and result is 0.60. Then I made a red one and the similarity between orange and red is 0.8. It compares colors too I suppose. At first when I checked the name, because there is "Structural", it only measured structure of the images but my little experiment shows that it measures colors too. Do you confirm this? $\endgroup$
    – Lyrk
    May 16 at 8:53
  • $\begingroup$ Do you really need to separate the image into its RGB components to get the exact similarity? Can't I use the original image and the image to be measured directly without separating into 3 components? $\endgroup$
    – Lyrk
    May 16 at 9:11
  • $\begingroup$ By design it only applied to a single channel image. What do you use to apply the comparison? If you use MATLAB, what it does is exactly as above with $ {w}_{r} = {w}_{g} = {w}_{b} = \frac{1}{3} $. $\endgroup$
    – Royi
    May 16 at 9:12
  • $\begingroup$ You don't need to separate as in 3 different arrays, just implement it in such way. $\endgroup$
    – Royi
    May 16 at 9:13
  • $\begingroup$ I use Scikit image in Python. github.com/scikit-image/scikit-image/blob/main/skimage/metrics/… There is a "multichannel" argument there. If I use it I hope I do not have to measure channels separately. $\endgroup$
    – Lyrk
    May 16 at 9:24

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