iambavith

Yes, some gains come from stripping metadata, but structural compression means optimizing how image data is encoded, like better Huffman tables for JPGs or smarter filtering for PNGs.

love the drag and drop to optimize approach. so cool.

yes tool works. its weird your images didn’t show any reduction in size. would love to try fixing it.

we did thought it through based on our use case :) that is working on bulk image processing where a slight loss in quality for Higher reduction in size is appreciated :) mostly for wallpaper apps or any image related sites. but yeah we will improvise to offer even cleaner approach

judging by the response we got , we will update site to reflect on “nearly lossless” approach than calling it “lossless” then We don’t mean to be deceitful.

the settings dump you dropped will not deliver on same outputs :)

wow. really love this. thank you for mentioning it. its super useful for uploading on government websites they really love limiting uploads

It's not a AI generated React bloat wrapper as you seem to think somehow.

We did not lie about actual working when fellow developers demanded answers. I love talking about what i have build. Maybe just nudge better? instead of hating?

we're using highly modified CompressorJS library to get this results. hope that helps.

I built the image compression for imgfiber using CompressorJS as the base. It’s a reliable library by Feng Yuanchen (https://fengyuanchen.github.io/compressorjs/), originally designed for lossy compression. I wrapped and modified it to meet our specific needs, tweaking the code so the results differ from the default - you won’t get the same file size or optimization when using it out of the box. Full credit for the core library goes to the original author; my work was to modify it for our usecase. to solve our own personal pain point.

Ohh, Optimage and ImageOptim are different. thanks for pointing that out! We just came acrross imageoptim thanks to your comment. interesting to see how it offers subscriptions.

JPEG-XL definitely way to go. we’re exploring ways to integrate support for formats like JXL and AVIF soon.

Squoosh is great but for average user dealing with so many peremeters feels overwhelming and provides odd results.When you're handling a large number of files - like a Midjourney archive or a collection of images,it’s more convenient to use an app with preset settings that can process them in bulk, rather than one with detailed options that require fine-tuning.

We focused on lossless techniques like metadata cleanup, optimal encoding settings, and structural compression—for both JPG and PNG.