Update README.md

README.md

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 license: cc-by-4.0
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+# BHI SISR Dataset
 
-512 tiling  
-COCO 2017 unlabeled dataset of 123’403 images into 8’814 512x512 tiles.  
-COCO 2017 train dataset of 118'287 images into 8'442 tiles.  
-inaturalist 2019 train_val dataset of 269'259 images into 238'524 tiles.  
-HQ50K 213'396 Tiles  
-ImageNet 197'436 Tiles  
-LSDIR 179'006 Tiles  
+The BHI SISR Dataset's purpose is for training single image super-resolution models and is a result of tests on my BHI filtering method, which I made [a huggingface community blogpost about](https://huggingface.co/blog/Phips/bhi-filtering).  
+
+TODO it consists of X images, which are all 512x512px dimensions and in the png format. 
+TODO visual example of the dataset  
+
+## Used Datasets
+
+This BHI SISR Dataset consists of the following datasets:  
+
+[HQ50K](https://github.com/littleYaang/HQ-50K)  
+[ImageNet](https://www.image-net.org/)  
+[FFHQ](https://github.com/NVlabs/ffhq-dataset)  
+[LSDIR](https://github.com/ofsoundof/LSDIR)  
+[DF2K](https://www.kaggle.com/datasets/thaihoa1476050/df2k-ost)  
+[OST](https://www.kaggle.com/datasets/thaihoa1476050/df2k-ost)  
+[iNaturalist 2019](https://github.com/visipedia/inat_comp/tree/master/2019)  
+[COCO 2017 Train](https://cocodataset.org/#download)  
+[COCO 2017 Unlabeled](https://cocodataset.org/#download)  
+[Nomosv2](https://github.com/neosr-project/neosr?tab=readme-ov-file#-datasets)  
+[HFA2K](https://github.com/neosr-project/neosr?tab=readme-ov-file#-datasets)  
+[Nomos_Uni](https://github.com/neosr-project/neosr?tab=readme-ov-file#-datasets)  
+[ModernAnimation1080_v3](https://huggingface.co/datasets/Zarxrax/ModernAnimation1080_v3)  
+
+
+## Tiling
+
+These datasets have then been tiled to 512x512px for improved I/O training speed, and normalization of image dimensions is also nice, so it will take consistent ressources if processing.  
+
+In some cases these led to fewer images in the dataset because they contained images with < 512px dimensions which were filtered out, some examples are:  
+COCO 2017 unlabeled from 123'403 images -> 8'814 tiles.  
+COCO 2017 train from 118'287 images -> 8'442 tiles.  
+
+And in some cases this led to more images, because the original images were high resolution and therefore gave multiple 512x512 tiles per single image.  
+For example HQ50K -> 213'396 tiles.  
+
+## Conversion
+
+If the images in the dataset were in the jpg format, they have been converted to png format using [Mogrify](https://imagemagick.org/script/mogrify.php).  
+
+## BHI Filtering
+
+I then filtered these sets with the BHI filtering method using the following thresholds:
+
+Blockiness < 30  
+HyperIQA >= 0.2  
+IC9600 >= 0.4  
+
+Which led to following dataset tile quantities that satisfied the filtering process, which made it into the BHI SISR Dataset:
 
-BHI Filtered Dataset consists of:  
 DF2K -> 12'639 Tiles  
 FFHQ -> 35'112 Tiles  
 HQ50K -> 61'647 Tiles  
@@ -18,4 +59,10 @@ ImageNet -> 4'505 Tiles
 LSDIR -> 116'141 Tiles  
 OST -> 1'048 Tiles  
 
-Then run oxipng -o 4 --strip safe --alpha *.png on it
+## Filename normalization
+
+All these subsets have been filename normalized, meaning 0.png, 1.png, 2.png, and so forth. They then have been merged into the BHI dataset, which folder has been normalized again.  
+
+## Optimization
+
+Then I used [oxipng](https://github.com/shssoichiro/oxipng) ("oxipng --strip safe --alpha *.png") for optimization.