7-Zip-zstd VS libjxl

Do you consider rar a standard? It's a pretty good format, even though there aren't any good open-source implementations. But if you're willing to pay for your software, WinRAR command line versions are available for most platforms.

7zip is the most obvious free alternative. There is also a 7zip fork that offers zstd [1]. The command line experience for 7zip isn't very good however.

1: https://github.com/mcmilk/7-Zip-zstd

Check out https://github.com/mcmilk/7-Zip-zstd

There's also this fork which supports additional compressions: https://github.com/mcmilk/7-Zip-zstd

7-zip is actually not a bad option, but I'd strongly advise using the fast-lzma2 codec, because, well, it is waay faster, and might even give you better ratios, depending on how you use it. You can find an implementation here, and it is compatible with traditional 7-zip (you can decompress it on any computer with any newish version of 7z)

Hi, PeaZip can use McMilk's additional compression codecs for 7z format (Zstandard, Brotli, Lizard, etc), https://github.com/mcmilk/7-Zip-zstd

You can extract the files yourself with 7Zip. You can install 7Zip from here and then install this plugin to extract ZStandard files, or you can directly install the modified 7Zip with the plugin already from that plugin page. Then simply open the zst file you downloaded with 7Zip and extract it.

https://github.com/libjxl/libjxl/blob/main/doc/format_overvi... is a pretty detailed but good overview. The highlights are variable size DCT (up to 128x128), ANS entropy prediction, and chroma from luminance prediction. https://github.com/libjxl/libjxl/blob/main/doc/encode_effort... also gives a good breakdown of features by effort level.

For JPEG XL, refer to its format overview [1]. In short its lossless mode uses a combination of multiple techniques: the rANS coding with an alias table, LZ77, reversible color transforms, a general vector quantization that subsumes palettes, a modified Haar transform and a learnable meta-adaptive decision tree for context modelling.

One good thing about JPEG XL is that its lossy mode also largely uses the same tool, with a major addition of specialized quantization and context modelling for low- and high-frequenty components.

[1] https://github.com/libjxl/libjxl/blob/main/doc/format_overvi...

The cjxl source is here. If you spot any reason why -x strip=exif may not work, tell me.

The problem is that the instructions for actually running the WASM file are not that clear... the docs the author mentions shows how to compile to WASM, which is easy enough, but then here's the instructions to make that actually work in the browser:

https://github.com/libjxl/libjxl/blob/main/tools/wasm_demo/R...

Yeah, you need some mysterious Python script, a JS service worker at runtime, choose whether you want the WASM or WASM_SIMD target, use a browser that supports Threads and SIMD if you chose that, make sure to serve everything with the appropriate custom HTTP headers... just reading that, I can see that to get this stuff working on non-browser WASM targets would likely require expertise in WASM, which is the point of the OP. WASM's UX is just not there yet.

https://github.com/libjxl/libjxl#usage

> Specifically for JPEG files, the default cjxl behavior is to apply lossless recompression and the default djxl behavior is to reconstruct the original JPEG file (when the extension of the output file is .jpg).

I mean compiling a bleeding edge kicad, inkscape or jpeg-xl is easy. But will probably trash your system if you already have an older version installed.

But you look at your image viewer that could have the lossless indicator? (and there is an issue open to add this indicator to the jxl files)

https://github.com/libjxl/libjxl/issues/432

You might want to use libjxl directly, e.g. for visually lossless images: cjxl --effort 9 --brotli_effort 11 --distance 1.0 --num_threads (nproc) --lossless_jpeg 0 input.png output.jxl on linux (if you're on windows/mac, just replace the (nproc) with the number of cpu threads you have, e.g. --num_threads 16).