libjpeg-turbo VS squoosh

> all decoders will render the same pixels

Not true. Even just within libjpeg, there are three different IDCT implementations (jidctflt.c, jidctfst.c, jidctint.c) and they produce different pixels (it's a classic speed vs quality trade-off). It's spec-compliant to choose any of those.

A few years ago, in libjpeg-turbo, they changed the smoothing kernel used for decoding (incomplete) progressive JPEGs, from a 3x3 window to 5x5. This meant the decoder produced different pixels, but again, that's still valid:

https://github.com/libjpeg-turbo/libjpeg-turbo/commit/6d91e9...

Last vestiges of this fact AFAIK were libjpeg, which had a macro NEED_SHORT_EXTERNAL_NAMES that shortens all public identifiers to have unique 6-letter-long prefixes. Libjpeg-turbo nowadays has removed them though [1].

[1] https://github.com/libjpeg-turbo/libjpeg-turbo/commit/52ded8...

While I think the move to safer code through Rust and other alternatives is a nice breath of fresh air, I doubt you can get these kinds of optimization without using unsafe code in Rust. These optimized implementations often require some kind of safety-bypassing memory modifications to work as efficiently ad they do.

There's a reason https://github.com/libjpeg-turbo/libjpeg-turbo/tree/main/sim... is filled with assembly files with conditional loading.

Sure. You'll see it very often in codec implementations. From rav1e, a fast AV1 encoder mostly written in Rust: https://github.com/xiph/rav1e/tree/master/src/x86

Large portions of the algorithm have been translated into assembly for ARM and x86. Shaving even a couple percent off something like motion compensation search will add up to meaningful gains.

Or the current reference implementation of JPEG: https://github.com/libjpeg-turbo/libjpeg-turbo/tree/main/sim...

zune-jpeg is 1.5x to 2x faster than jpeg-decoder and is on par with libjpeg-turbo.

This is interesting from the standpoint of how new JVM features can be used to improve performance (what I presume the article's main purpose to have been), but the image processing improvement itself isn't head-turning. Also, we've found that libjpeg-turbo (https://libjpeg-turbo.org/) is ~5x (IIRC, can re-run my JMH benchmark if anyone wants me to) as fast for decoding JPEGs as ImageIO, so we wouldn't even benefit from this change in 21 much.

libjpeg-turbo: https://github.com/libjpeg-turbo/libjpeg-turbo/blob/main/simd/x86_64/jsimdcpu.asm

libjpeg-turbo

For all but the simplest formats (basically BMP), the difficulty of implementing encoding/decoding from scratch is significant - well beyond a beginner's ability, and challenging/time-consuming even for senior developers. So, libraries are used in practice - e.g. libpng and libjpeg-turbo.

Here's another handy tool that I use: https://squoosh.app/

Squoosh: A webpage that allows you to quickly optimize images for your blog.

One of the most complete image compressor out there, squoosh.app by Google, uses web assembly for decoding/encoding images and it works pretty well.

First off you want to shrink your images. Every mb your page is the more it will hurt your score. I use https://squoosh.app/

https://squoosh.app/

Having a quick look at squoosh, it uses lossy compression of webp by default.

Size images appropriately (https://squoosh.app/ can be used for this). Ideally, the size of the image should be kept below 100 KB.

if your images a large file size, remove them from the page, reduce file size, and place new reduced file size images in their place - publish the site to bring the new pics live - clear your cache - go the page and test it (good tool to reduce image file size is Google Squoosh - https://squoosh.app/ )

I'm just exporting normally from Figma, but then using squoosh.app (browser based) to adjust quality/compression and even for making sure png files are optimized for prod.

Hey I see your friend made a clan, but this guy found a workaround here if you still need it. I just used https://squoosh.app/ to basically save a new copy of the image and that worked