guetzli VS google-research

JPEGLI = A small JPEG

The suffix -li is used in Swiss German dialects. It forms a diminutive of the root word, by adding -li to the end of the root word to convey the smallness of the object and to convey a sense of intimacy or endearment.

This obviously comes out of Google Zürich.

Other notable Google projects using Swiss German:

https://github.com/google/gipfeli high-speed compression

Gipfeli = Croissant

https://github.com/google/guetzli perceptual JPEG encoder

Guetzli = Cookie

https://github.com/weggli-rs/weggli semantic search tool

Weggli = Bread roll

https://github.com/google/brotli lossless compression

Brötli = Small bread

A JPEG encoder developed by Jyrki Alakujala, Robert Obryk, and Zoltán Szabadka, and released by Google in 2017. Guetzli specializes in high-end image quality where it is claimed to produce significantly smaller files than prior encoders at equivalent quality, albeit at very low speed. It is named after the Swiss German expression for biscuits, in line with the names of other compression technology from Google. github.com/google/guetzli

I'm a huge fan of AV1 for video, but for images JPEG-XL is simply the better codec than AVIF. If you've not actually looked closely at a comparison and are just on the side of AVIF in this debate because it's based on AV1 (and maybe you hate HEVC / HEIC), I'd urge you to look closer. Jpeg XL is pretty unrelated to Jpeg, Jpeg 2000 and Jpeg XR and instead a successor of Google Guetzli, FLIF and newer research.

I've never had much luck using jpegoptim. In most cases it's only removing the metadata, which isn't much on high-res files.

Guetzli is nice, if you don't have too many images to recompress (quite slow): https://github.com/google/guetzli

If you're going for full re-encoding, it might help to decode the current JPEG with https://github.com/google/knusperli ... but if you re-JPEG that you might have second-order artifacts. Give it a try. Then compress with https://github.com/google/guetzli

You know. I was actually quite annoyed ( to say the least ) with the post. For one the post is lacking a date, and you have to search yourself it was published in April 2017. And without a date the article is completely lacking context because Guetzli [1] hasn't been worked on for 5 years. And as [2] mentioned its work and derivative was ultimately merged into JPEG XL, which is a very decent image format. ( People should definitely check out JPEG XL if it is not on your radar yet )

But then I notice it was Dan luu who submitted it, which likely means there must be something much deeper than is what is shown on the surface. So what is the context here ?

[1] https://github.com/google/guetzli

[2] https://news.ycombinator.com/item?id=30622303

People on here will be happy to say that I do a similar thing, however my sequence length is dynamic because I also use a 2nd data structure - I'll use pretentious academic speak: I use a simple bigram LM (2-gram) for single next-word likeliness and separately a trie that models all words and phrases (so, n-gram). Not sure how many total nodes because sentence lengths vary in training data, but there are about 200,000 entry points (keys) so probably about 2-10 million total nodes in the default setup.

"Constructing 7-gram LM": They likely started with bigrams (what I use) which only tells you the next word based on 1 word given, and thought to increase accuracy by modeling out more words in a sequence, and eventually let the user (developer) pass in any amount they want to model (https://github.com/google-research/google-research/blob/5c87...). I thought of this too at first, but I actually got more accuracy (and speed) out of just keeping them as bigrams and making a totally separate structure that models out an n-gram of all phrases (e.g. could be a 24-token long sequence or 100+ tokens etc. I model it all) and if that phrase is found, then I just get the bigram assumption of the last token of the phrase. This works better when the training data is more diverse (for a very generic model), but theirs would probably outperform mine on accuracy when the training data has a lot of nearly identical sentences that only change wildly toward the end - I don't find this pattern in typical data though, maybe for certain coding and other tasks there are those patterns though. But because it's not dynamic and they make you provide that number, even a low number (any phrase longer than 2 words) - theirs will always have to do more lookup work than with simple bigrams and they're also limited by that fixed number as far as accuracy. I wonder how scalable that is - if I need to train on occasional ~100-word long sentences but also (and mostly) just ~3-word long sentences, I guess I set this to 100 and have a mostly "undefined" trie.

I also thought of the name "LMJS", theirs is "jslm" :) but I went with simply "next-token-prediction" because that's what it ultimately does as a library. I don't know what theirs is really designed for other than proving a concept. Most of their code files are actually comments and hypothetical scenarios.

I recently added a browser example showing simple autocomplete using my library: https://github.com/bennyschmidt/next-token-prediction/tree/m... (video)

And next I'm implementing 8-dimensional embeddings that are converted to normalized vectors between 0-1 to see if doing math on them does anything useful beyond similarity, right now they look like this:

  [nextFrequency, prevalence, specificity, length, firstLetter, lastLetter, firstVowel, lastVowel]

The change was literally just made: https://github.com/google-research/google-research/commit/4a...

It appears this was in response to Hacker News comments.

There are options such as Google's ScaNN that may let you go farther before needing to consider specialized databases.

https://github.com/google-research/google-research/blob/mast...

I feel it was unnecesary to create this because https://research.google/ already exists? It just seems like they want to take another URL with a "pure" domain name instead of psubdirectories, etc parts.

https://github.com/google-research/google-research/blob/mast...

You could also try some dedicated translation models like https://huggingface.co/facebook/nllb-moe-54b (or https://github.com/google-research/google-research/tree/master/madlad_400 for something smaller) and see how they do.

Google released T5X checkpoints for MADLAD-400 a couple of months ago, but nobody could figure out how to run them. Turns out the vocabulary was wrong, but they uploaded the correct one last week.