From 86493d386612ea612da6ff76b4b49f0b535396fd Mon Sep 17 00:00:00 2001 From: jh Date: Wed, 17 Mar 2021 14:18:27 -0700 Subject: [PATCH] docs: update docs for ms-ssim --- doc/features/ms-ssim/README.md | 42 +++++++++++++++++++++++++++++ doc/features/random-color/README.md | 5 +++- 2 files changed, 46 insertions(+), 1 deletion(-) create mode 100644 doc/features/ms-ssim/README.md diff --git a/doc/features/ms-ssim/README.md b/doc/features/ms-ssim/README.md new file mode 100644 index 0000000..c7a0476 --- /dev/null +++ b/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 +``` + + + diff --git a/doc/features/random-color/README.md b/doc/features/random-color/README.md index 8d8298c..d1aeac1 100644 --- a/doc/features/random-color/README.md +++ b/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 +``` +