6 releases

Uses new Rust 2021

new 0.2.2	May 14, 2022
0.2.1	May 6, 2022
0.2.0-RC2	Apr 24, 2022
0.1.1	Apr 6, 2022

#95 in Images

487 downloads per month

MIT license

39KB
802 lines

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: $1-\sqrt{\frac{(\sum_{x,y=0}^{x,y=w,h}\left(f(x,y)-g(x,y)\right)^2)}{w*h}}$
- By MSSIM
  - SSIM is implemented as described on wikipedia: $\mathrm{SSIM}(x,y)={\frac {(2\mu _{x}\mu _{y} c_{1})(2\sigma _{xy} c_{2})}{(\mu _{x}^{2} \mu _{y}^{2} c_{1})(\sigma _{x}^{2} \sigma _{y}^{2} c_{2})}}$
  - 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: $\mathrm{score}=\mathrm{avg}_{x,y}\left(\mathrm{min}\left[\Delta \mathrm{MSSIM}(x,y),1- \sqrt{(1-\Delta RMS(u,x,y))^2 (1-\Delta RMS(v,x,y))^2}\right]\right)$
  - This allows for a good separation of color differences and structure differences
Comparing grayscale images by histogram
- Several distance metrics implemented see OpenCV docs:
  - Correlation $d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}$
  - Chi-Square $d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}$
  - Intersection $d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))$
  - Hellinger distance $d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\int{H_1} \int{H_2}}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}$

Dependencies

~7.5MB
~114K SLoC