Prediction-Encoded Pixels image format | Hacker News Reader

Prediction-Encoded Pixels image format

github.com

53 pointsmsephton11 comments

I really needs more benchmarks, especially decompression time. Also the sizes are interesting for very small images, but for real images, there are maybe better lossy variants:

  nz_scene - PEP = 73.542 bytes,
       lossy-PNG = 43.557 bytes,
      lossy-WEBP = 26.654 bytes,
  lossy-mozcjpeg = 15.716 bytes

So it's not about filesize here, it must be decompression speed.

The creator says that the PEP image format is meant for small, limited colour images and of course it does lossless compression.

Thanks for making that clear. But is it worth the hassle?

https://nigeltao.github.io/blog/2021/fastest-safest-png-deco...

PNG decoding seems to be fast enough:

  tree1    - PEP =  0.412 ms PNG = 0.25 ms
  font     - PEP =  0.602 ms PNG = 0.663 ms
  nz_scene - PEP = 32.121 ms PNG = 3.069 ms

Anyway, PEP is interesting!

I don't see any hassle, really. It's just another image format: good for some use cases, bad for others. No one file format is perfect. It was interesting enough for me to give it a couple of hours to implement a cli and add support to my pixel app.

It's a lossless format optimized for file size rather than decompression speed; the README seems clear enough. Made by a pixel art game dev, for compressing sprites in pixel art games, so I assume it fits a useful niche.

I've put together a quick CLI for macOS and a very naïve benchmarking suite to see what the gains are like. Compared to optimised PNG, it's worse most of the time but the interesting thing is when it is better. I'm not sure there's a hard and fast rule, at least not that I've found so far.

Link?

pepr on GitHub https://github.com/gingerbeardman/pepr (macOS-only)

Also I posted some results earlier today: https://twitter.com/gingerbeardman/status/195993422257285161...

And I already added support to my pixel art app, Dottie, just for kicks https://twitter.com/gingerbeardman/status/195998796230676915...

So from what I understand, it predicts the next pixel by looking at the two previous pixels and counting what has appeared in that context earlier in the image. I'm not 100% sure, but I think it processes the pixels line by line.

It then encodes with arithmetic encoding. Along with the palette, I think that does most of the heavy lifting in the compression, an adaptive model looking at just two pixels of context shouldn't make especially good predictions. PNG (sort of) looks at 4, the three above and the one to the left.

If someone makes a presentation about this format, it will be a PEP talk.

</philosoraptor>

Endesga is always cooking up something interesting.

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