[SimplyUnknown]

I wonder how this compares to FLIF. I also tried to compress images based on shape and structure but by approximating these using skeletons.

I'm just a bit struggling with their performance comparison. The graphs they present are very pretty and promising but for the presented images we're quite left in the dark. They dump some images and theirs looks prettier and the authors give us some indication of quality but it's not conclusive evidence that their method produces better images. Typically when different compressed images are presented two things can vary: quality and file-size. In the presented images both seem to varying without telling us which is which. Also, there is no baseline to compare against, either in terms of filesize or what the should look like. Sure, we humans can make a very educated guess but it is just sloppy to not include the uncompressed original image.

I will be fully convinced when I can try it for myself on my own image set.

[bhouston]

FLIF is lossless. So very very different.

[vanderZwan]

Err... FLIF is intended for lossless compression, but can be lossy (it doesn't perform as well as intentionally lossy codecs, although that might also be a matter of optimising for it).

However, it also has a kind of adaptive ML-ish approach so it might be technically similar.

> FLIF is based on MANIAC compression. MANIAC (Meta-Adaptive Near-zero Integer Arithmetic Coding) is an algorithm for entropy coding developed by Jon Sneyers and Pieter Wuille. It is a variant of CABAC (context-adaptive binary arithmetic coding), where instead of using a multi-dimensional array of quantized local image information, the contexts are nodes of decision trees which are dynamically learned at encode time. This means a much more image-specific context model can be used, resulting in better compression.

http://flif.info/