Alternatives
Products that do what AvifHash v0 – Compact and efficient image placeholders does
AvifHash leverages the power of AVIF to create image placeholders that are both compact and efficient. This Proof of Concept shows promising results: at 27 characters, AvifHash outperforms BlurHash https://blurha.sh/ (using 4x3 components) in quality and detail retention. At a similar quality, BlurHash needs 54 (5x5) to 76 characters (6x6 components). Given that AVIF decoding is done by the web engine, AvifHash is very small: the entire demo page (including parsing and re-hydration code) is only 2.3 kB gzipped.
- 1IM
2019 · github.com
- 2
Your pics converted & compressed to multiple formats locally
2024
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- 6BD
2020 · github.com
- 7CB
2021 · github.com
- 8AA
2017 · github.com
- 9CA
2022 · optimizeimages.com
- 10AB
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
- 11ME
2014 · mozjpeg.codelove.de
- 12H4
2018 · usenix.org
- 13MS
2013 · w-labs.at
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Started with a pure JS implementation (still the fastest JS image diff), but wanted to push performance further. I rewrote the core in Rust to make it the fastest open-source single-threaded image diff. On 4K images (5600×3200): ~327ms vs odiff's ~1215ms. Binaries are ~3x smaller too (~700KB vs ~2MB). The core insight: make the cold pass smarter to make the hot pass do less work. Instead of simple pixel equality, the cold pass scans dynamic-sized blocks and marks "problematic" ones - blocks that might contain differences. The hot pass then only runs YIQ perceptual diff and antialiasing check…
Dec 2025 · github.com
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Free, private image converter — 100% in your browser
11d ago · convertidor.academiadineroen60.com
- 17SS
Hi HN, I’m building SEE (Semantic Entropy Encoding): a searchable compression format for JSON/NDJSON. Goal: reduce the “data tax” (storage/egress) and “CPU tax” (decompress/parse) by keeping JSON searchable while compressed, with page-level random access. I just published a proof-first evaluation release: Offline DEMO ZIP (~10 min): prints compression ratios + skip rates + lookup latency (p50/p95/p99) DD pack: audit/repro evidence (decode mismatch=0, extended mismatch=0, audit PASS) Latest release:…
Feb 2026 · gitlab.com
- 18BH
Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
2019
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Thought I'd dive into this cool streaming/approximation algorithms problem I encountered a few months ago. TLDR: we can create our own custom floating point representation for encoding and decoding integers and use this to index into a tiny 2D histogram, upper-bounding approximation error based on the number of bits per integer we specify to keep. --- Broadly useful for aggregating statistics from massive data streams of user data. Also turns out this is incredibly similar to data structures used in production, like HDRHistogram.
Jan 2026 · alexkranias.com
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Shrink images without losing quality, right in your browser
18d ago · image-compressor-saas.shop
- 21SL
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…
2024 · github.com
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We’ve just released SemHash v0.3.0, a major rework of our open-source text pre-processing library. We’ve added two new functionalities: outlier filtering & representative sampling. The core API has been reworked to make sure all of these features can be used together in an intuitive way. Our new features use the existing approximate nearest neighbors index that we already used for semantic deduplication, so they can be ran very quickly after building the index on your dataset. The core package can now be used for: - Semantic Deduplication: Remove semantic duplicates from your dataset. This…
2025 · github.com
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Convert, resize, optimize images privately in your browser
29d ago · freewww.com
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