image-compare 0.2.0-RC1

Image comparison library based upon the image crate. Currently it provides SSIM and RMS for comparing grayscale and rgb images as well as several grayscale histogram distance metrics. All with a friendly license.
Documentation 
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# image-compare
[![Documentation](https://docs.rs/image-compare/badge.svg)](https://docs.rs/image-compare)
![CI](https://github.com/ChrisRega/image-compare/actions/workflows/rust.yml/badge.svg?branch=main "CI")

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 no SIMD or GPU usage.

### 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.
- 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)}}">
     
### Planned:
- Histogram comparison for RGB images
- SIMD for RMS and MSSIM