docs: update docs for ms-ssim

doc/features/ms-ssim/README.md

@@ -0,0 +1,42 @@
+# Multiscale SSIM (MS-SSIM)
+
+Allows you to train using the MS-SSIM (multiscale structural similarity index measure) as the main loss metric,
+a perceptually more accurate measure of image quality than MSE (mean squared error).
+
+- [DESCRIPTION](#description)
+- [USAGE](#usage)
+
+![](example.png)
+
+## DESCRIPTION
+
+[SSIM](https://en.wikipedia.org/wiki/Structural_similarity) is metric for comparing the perceptial quality of an image:
+> SSIM is a perception-based model that considers image degradation as perceived change in structural information, 
+> while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. 
+> [...]
+> Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially 
+> close. These dependencies carry important information about the structure of the objects in the visual scene. 
+> Luminance masking is a phenomenon whereby image distortions (in this context) tend to be less visible in bright 
+> regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant 
+> activity or "texture" in the image.
+
+The current loss metric is a combination of SSIM (structural similarity index measure) and 
+[MSE](https://en.wikipedia.org/wiki/Mean_squared_error) (mean squared error).
+
+[Multiscale SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Multi-Scale_SSIM) is a variant of SSIM that
+improves upon SSIM by comparing the similarity at multiple scales (e.g.: full-size, half-size, 1/4 size, etc.)
+By using MS-SSIM as our main loss metric, we should expect the image similarity to improve across each scale, improving
+both the large scale and small scale detail of the predicted images.
+
+Original paper: [Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. 
+"Multiscale structural similarity for image quality assessment." 
+Signals, Systems and Computers, 2004.](https://www.cns.nyu.edu/pub/eero/wang03b.pdf)
+
+## USAGE
+
+```
+[n] Use multiscale loss? ( y/n ?:help ) : y
+```
+
+
+

doc/features/random-color/README.md

@@ -19,4 +19,7 @@ maintaining the same `C*` (chroma, relative saturation).
 
 ## USAGE
 
-`[n] Random color ( y/n ?:help ) : y`
+```
+[n] Random color ( y/n ?:help ) : y
+```
+