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Comment by arp242 | Hacker News Reader

Comparison of "zpng" (PNG wth zstd) and WebP lossless, with current PNG. From https://github.com/WangXuan95/Image-Compression-Benchmark :

  Compressed format    Compressed size (bytes)  Compress Time  Decompress Time
  WEBP (lossless m5)   1,475,908,700           1,112          49
  WEBP (lossless m1)   1,496,478,650             720          37
  ZPNG (-19)           1,703,197,687           1,529          20
  ZPNG                 1,755,786,378              26          24

  PNG (optipng -o5)    1,899,273,578           27,680         26
  PNG (optipng -o2)    1,905,215,734            4,395         27
  PNG (optimize=True)  1,935,713,540            1,120         29
  PNG (optimize=False) 2,003,016,524              335         34

Doesn't really seem worth it? It doesn't compress better, and only slightly faster in decompression time.

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"Only slightly faster in decompression time."

m5 vs -19 is nearly 2.5x faster to decompress; given that most data is decompressed many many more times (often thousands or millions of times more, often by devices running on small batteries) than it is compressed, that's an enormous win, not "only slightly faster".

The way in which it might not be worth it is the larger size, which is a real drawback.

The difference is barely noticeable in real-world cases, in terms of performance or battery. Decoding images is a small part of loading an entire webpage from the internet. And transferring data isn't free either, so any benefits need to be offset against the larger file size and increased network usage.

When you talk about images over HTTP, you need to consider most web servers and browsers already support zstd compression on the transport, so the potential bandwidth win provided by zstd is already being made use of today.

I'm not sure how that's relevant for a new "ZPNG" format vs. lossless WebP?

Win how?

More efficiency will inevitably only lead to increased usage of the CPU and in turn batteries draining faster.

https://en.wikipedia.org/wiki/Jevons_paradox

So someone is going to load 2.5x as many images because it can be decoded 2.5x faster? The paradox isn't a law of physics, it's an interesting observation about markets. (If this was a joke it was too subtle for me)

Might as well just shoot yourself if that's how you look at improvements. The only way to do something good it to stop existing. (this is a general statement, not aimed at you or anyone in particular)

you have to do the math - do you have more bandwidth or storage or cpu?

Not related to images, but I remember compressing packages of executables and zstd was a clear winner over other compression standards.

Some compression algorithms can run in parallel, and on a system with lots of cpus it can be a big factor.

Am I reading those numbers right? That's like 25x faster compression than WEBP-M1, there's probably a use case for that.

The numbers seem small enough that it will rarely matter, but I suppose there might be a use case somewhere?

But lets be real here: this is basically just a new image format. With more code to maintain, fresh new exciting zero-days, and all of that. You need a strong use case to justify that, and "already fast encode is now faster" is probably not it.

I don’t think it’s quite as bad, though? It’s using a known compression library that (from reading other comments) has seen use and testing. The rest of PNG would remain unchanged, as the decompression format is a plugin.

I know it needs to be battle tested as a single entity but it’s not the same as writing a new image format from scratch.

Considering both zstandard and PNG are already web facing technologies, would the combination of both really increase the attack surface?

And compresses significantly faster than regular png too.

That’s the major advantage of zstd, fast compression. Not particularly relevant in web use cases, but would be great for saving screenshots.

Is webp really losses here? As far as i remember its capped at 4:2:0 and can't do 4:4:4 files without loosing some of the color data

I looked it up and lossy mode only supports 4:2:0 but the lossless mode uses RGBA.

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