[schobi]

This would have been nice if code was included, but I did not see it. It is just the statistics of the specific Kodim dataset, that was once published on a Photodisc.

Use cases for this? Maybe to validate you own statistics computation?

The Kodim dataset is pretty useless nowadays. If you build an algorithm that runs on this dataset (or the Lena image) it will be pretty bad on other images. Think of data dependent things like compression, segmentation, inpainting or such. The specific characteristics of these "small" images are unlike a cellphone Jpeg or DSLR raw.

[PearsonZero]

Code is now in the repo — ‘’’pip install numpy Pillow’’’ where you can duplicate the values.

Kodim might seem outdated, but it’s still the primary benchmark cited in learned image compression research (CLIC, neural codec papers) and is referenced across hundreds of published works.

The question isn’t whether newer capture pipelines produce different data — they do — but whether the research community understands the statistical structure of the benchmark it’s been using for thirty years.

Think of data dependent things like - compression?