Image Compressor

How to use this image compressor

1. Choose an image file.
2. Adjust the quality slider to balance size and clarity.
3. Click "Download" to save the compressed version.

How does this work?

Your image never leaves your device. It is redrawn onto an HTML canvas and re-encoded at the quality level you choose, entirely inside your browser.

What "quality" actually changes in a compressed image

Lossy compression formats like JPEG don't simply throw away random pixels — they exploit specific weaknesses in human vision. Two techniques do most of the work: chroma subsampling, which stores color detail at a lower resolution than brightness detail (since human eyes are far more sensitive to brightness changes than color changes), and quantization, which discards fine, high-frequency detail in blocks of the image that the algorithm predicts won't be noticeable. Lowering the quality slider makes both effects more aggressive — at low settings, this becomes visible as blocky artifacts around sharp edges and a loss of fine texture, which is why photos with soft gradients (skies, skin tones) tend to compress much more gracefully than images with sharp text or fine line art.

JPEG vs. PNG vs. WebP — pick the format before you compress

Compression quality only matters once you've picked the right format, and the three most common web image formats aren't interchangeable. JPEG is lossy and has no transparency support, but it's excellent at compressing photographs with lots of natural color variation. PNG is lossless — it never loses detail no matter how it's re-saved — and supports transparency, which makes it the right choice for logos, icons, and screenshots with sharp edges and flat colors, but a poor choice for photos, where it produces much larger files than JPEG for similar visual quality. WebP is newer and generally outperforms both: it supports lossy and lossless compression plus transparency in one format, typically producing smaller files than an equivalent-quality JPEG or PNG, though it has slightly less universal support in older software than the two long-established formats.

Why some images barely shrink at all

An image that's already been through lossy compression once doesn't compress much further the second time — most of the "wasteful" detail a compressor can remove was already stripped out in the first pass, so re-compressing an already-compressed JPEG mostly just adds a second round of quality loss without much file size benefit. Screenshots and images with large areas of flat, uniform color (interface screenshots, simple illustrations, text-heavy graphics) also tend to compress less dramatically as JPEGs than as PNGs, since JPEG's strengths are built around the kind of gradual color variation found in photographs, not sharp edges and solid color fields — for that kind of image, trying PNG or WebP usually beats trying to force a smaller JPEG through a lower quality setting.

Dimensions vs. quality — two different levers

Quality and pixel dimensions affect file size through completely different mechanisms, and confusing the two leads to disappointing results. Lowering quality keeps the same number of pixels but stores less detail per pixel; reducing dimensions actually removes pixels, which usually has a much bigger impact on file size for images that are larger than they need to be for their actual display size. A 4000-pixel-wide photo displayed at 800 pixels wide on a web page is carrying roughly 25 times more pixel data than it needs, and no amount of quality slider adjustment fixes that — resizing to the actual display dimensions first, then adjusting quality, gets a far better result than quality adjustment alone.

Limitations of this tool

This compressor works by redrawing your image onto an HTML canvas and re-encoding it using the browser's built-in image encoder, entirely in memory — it doesn't change image dimensions, doesn't offer format conversion (a JPEG stays a JPEG), and doesn't expose the more advanced tuning options that dedicated encoders like mozjpeg or specialized WebP tools provide, such as separate chroma subsampling controls or perceptual quality metrics. Very large source images (many tens of megapixels) can also be slow to process or hit browser memory limits, since the entire image has to be decoded and held in memory during re-encoding.