# image-compare
[](https://docs.rs/image-compare)
Simple image comparison in rust based on the image crate
Note that this crate is heavily work in progress. Algorithms are neither cross-checked not particularly fast yet.
Everything is implemented in plain CPU with rayon multithreading.
SIMD is under investigation on a feature branch (simd-experimental).
### Supported now:
- Comparing grayscale and rgb images by structure
- By RMS - score is calculated by: <img src="https://render.githubusercontent.com/render/math?math=1-\sqrt{\frac{(\sum_{x,y=0}^{x,y=w,h}\left(f(x,y)-g(x,y)\right)^2)}{w*h}}">
- By MSSIM
- SSIM is implemented as described on [wikipedia](https://en.wikipedia.org/wiki/Structural_similarity): <img src="https://render.githubusercontent.com/render/math?math=\mathrm{SSIM}(x,y)={\frac {(2\mu _{x}\mu _{y}+c_{1})(2\sigma _{xy}+c_{2})}{(\mu _{x}^{2}+\mu _{y}^{2}+c_{1})(\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2})}}">
- MSSIM is calculated by using 8x8 pixel windows for SSIM and averaging over the results
- RGB type images are split to R,G and B channels and processed separately.
- The worst of the color results is propagated as score but a float-typed RGB image provides access to all values.
- As you can see in the gherkin tests this result is not worth it currently, as it takes a lot more time
- It could be improved, by not just propagating the individual color-score results but using the worst for each pixel
- This approach is implemented in hybrid-mode, see below
- "hybrid comparison"
- Splitting the image to YUV colorspace according to T.871
- Processing the Y channel with MSSIM
- Comparing U and V channels via RMS
- Recombining the differences to a nice visualization image
- Score is calculated as: <img src="https://render.githubusercontent.com/render/math?math=\mathrm{score}=\mathrm{avg}_{x,y}\left(\mathrm{min}\left[\Delta \mathrm{MSSIM}(x,y),1- \sqrt{(1-\Delta RMS(u,x,y))^2 + (1-\Delta RMS(v,x,y))^2}\right]\right)">
- This allows for a good separation of color differences and structure differences
- Comparing grayscale images by histogram
- Several distance metrics implemented see [OpenCV docs](https://docs.opencv.org/4.5.5/d8/dc8/tutorial_histogram_comparison.html):
- Correlation <img src="https://render.githubusercontent.com/render/math?math=d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}">
- Chi-Square <img src="https://render.githubusercontent.com/render/math?math=d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}">
- Intersection <img src="https://render.githubusercontent.com/render/math?math=d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))">
- Hellinger distance <img src="https://render.githubusercontent.com/render/math?math=d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\int{H_1} \int{H_2}}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}">