Alternatives
Products that do what SQZ – Low complexity, scalable lossy to lossless image compression does
I'd like to share a little toy project of mine, a really simple image codec that can do lossy to full lossless image compression with complete scalability at a byte level granularity - you can compress an image just once, even fully losslessly if needed, and then get any lossy version possible by simply stopping decompression at any offset in the compressed data. This "encode onde, serve many" approach is especially interesting for providing downscaled low quality image previews (LQIP) in as tight a storage budget as possible, and then allowing seamless, transparent refinement as deemed…
- 1IM
2019 · github.com
- 2TA
2015 · ruarai.github.io
- 3MA
I've spent the last few months working on this codec. It has the following characteristics: - SOTA decompression throughput in its ratio class - Decent ratios (comparable to LZ4 at high effort levels) - Slow compression Most of the gains can be attributed to reducing branches and making decompression very friendly to out-of-order cores, by using a smart format. Results on the tarred Silesia corpus on Intel x86-64 follow: codec decode ratio encode misa77 -0 5219 MB/s 42.64% 54.5 MB/s misa77 -1 4274 MB/s 39.65% 51.2 MB/s lz4 2505 MB/s 47.59% 371 MB/s lz4hc -12…
Jul 2026 · github.com
- 4HH
2020 · hific.github.io
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I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files. Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output. On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte). It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my…
Jun 2026
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Hi, so over the weekend I built a free to use service to compress one or more images using MozJEPG encoding enabled via WASM. It's a static site, all the processing happens on the client side, no file uploads or sign-ups needed! Why? The existing services are either filled with ads or require you to sign-up / pay. would greatly appreciate any feedback! TIA!
2024 · captaindavinci.github.io
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2014 · mozjpeg.codelove.de
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2018 · dropbox.github.io
- 14IC
This is an upgrade of a tool I created 15 years ago in Python to learn OOP and solve some inadequacies in the HDR stacking tools I could find at the time. The problem was, none of them were really "batch friendly". None of them properly preserved the metadata I wanted them to stuff into the output file. There were probably some other reasons also, I just can't remember them now. It got the job done, but was very slow. Python was what I knew at the time and even with NumPy, I was limited in the speed I could squeeze out of it. (I also made some very specific, conscious, architectural choices…
Jun 2026 · github.com
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- 16MS
2013 · w-labs.at
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2019 · github.com
- 18AA
2014 · tapcelerate.com
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Image optimizer — no uploads, all in-browser
Jul 2026 · image-compression-hazel.vercel.app
- 20TE
2021 · github.com
- 21AB
hey there hn! thought you would appreciate my brand new file format - i've spent over 40 minutes creating this standard. it's really hard trying to get your ideas shown to standards boards etc but i think with the great community here we can help get bioif support into all major browsers :) the spec is available over at the website, as is a reference implementation. it already has higher usage than jpeg-xl. - best
2025 · image.bsb.dev
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https://github.com/eigenloss/isthisloss Original thread: https://news.ycombinator.com/item?id=16907615
2018
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