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Batch image resizer

Drop up to 50 images at once. Set width, height, or scale — choose output format and quality — then download everything as a single ZIP file. Your images never leave your device.

Drop images here or browse

PNG, JPG, WebP, GIF, BMP — up to 50 images

How batch resizing works

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Drop a folder

Select multiple files or drag-drop a whole folder. Up to 50 images are processed per batch.

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Set dimensions

Resize by fixed width, height, fit inside a box, or scale by percentage. Aspect ratio is preserved automatically.

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Pick format

Keep original format or convert everything to JPEG, PNG, or WebP. Quality slider controls compression level.

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Download ZIP

All processed images are bundled into a single ZIP file for instant download. Nothing uploaded to any server.

Downscaling works, upscaling mostly does not

Reducing an image's dimensions is a well-posed problem: you have more information than you need and must combine it sensibly. A good resampling filter averages contributions from each source pixel, so detail is condensed rather than discarded, and the result is genuinely sharp.

Enlarging is the opposite. The extra pixels are not recorded anywhere, so any algorithm must invent them by interpolating between neighbours. Bicubic interpolation produces a smooth, soft result; nearest-neighbour produces hard blocky edges, which is exactly right for pixel art and wrong for everything else. Neither adds detail, because the detail does not exist.

As a working limit, enlarging by up to about 150 percent is usually acceptable for screen use, and beyond roughly 200 percent softness becomes obvious. Machine-learning upscalers do better by hallucinating plausible detail from training data, which is convincing for textures and unreliable for text, faces and anything where accuracy matters.

Aspect ratio and the arithmetic of pixel counts

Changing width and height independently distorts the image, and the human eye is remarkably good at detecting it — a face stretched by five percent reads as subtly wrong before anyone can say why. Lock the ratio unless distortion is deliberate.

When a target ratio differs from the source, you must either crop or pad, and the choice is editorial. Cropping keeps the subject at full size but loses edges, which is fine for a background and destructive for a group photograph. Padding keeps everything and adds bars, which suits logos and product shots on a white background.

It is worth remembering that pixel count scales with the square of the linear dimension. Halving both width and height leaves a quarter of the pixels, so a 4,000 × 3,000 photograph reduced to 1,000 × 750 has one sixteenth the data — which is why resizing before compressing is far more effective than compressing harder at full size.

Sizing for actual displays

Screen images should be sized to their display dimensions, not their source dimensions. Serving a 4,000-pixel photograph into a 800-pixel column wastes bandwidth and forces the browser to resample on every layout, which costs memory and time on low-end devices.

High-density displays complicate this. A device with a 2× pixel ratio wants roughly twice the pixels of its CSS layout size, so an 800-pixel-wide slot ideally receives a 1,600-pixel image. Going beyond 2× has rapidly diminishing returns — 3× is essentially indistinguishable at normal viewing distances while costing another 125 percent in pixels.

The right pattern for the web is a responsive srcset offering two or three widths and letting the browser choose. If you are producing one file, target twice the largest layout width you expect and compress a little harder to compensate; downscaling in the browser is visually forgiving, while upscaling is not.

Batch image resizer FAQ

How do I know what size my images will end up?

The table shows it before you run anything. Each row lists the original pixel dimensions and the exact output dimensions for the settings you have chosen, updating as you type. That matters most on a mixed batch: "Width 1280" turns a 3000 × 2000 photo into 1280 × 853 and a 4000 × 1000 panorama into 1280 × 320, and those are very different results from one setting.

Will small images get blown up?

Not by default. Don't enlarge smaller images is on, so anything already smaller than your target is left at its original size rather than being upscaled into a blurry copy. Untick it if you genuinely want everything forced to the same dimensions — the table will say "enlarged 2.6×" so you can see it happening.

Are my images uploaded to a server?

No. Every image is processed entirely inside your browser using the Canvas API. Your files never leave your device and the tool works offline after the page has loaded.

Does "fit inside" preserve aspect ratio?

Yes. Fit inside scales the image down so that neither width nor height exceeds the values you set, while keeping the original aspect ratio. Images smaller than the target are not upscaled.

What is the maximum file size per image?

There is no enforced limit. Very large images (over 20 MP) may be slow to process in the browser due to Canvas memory limits, but there is no server-side restriction.

Can I process animated GIFs?

The tool processes only the first frame of animated GIFs. For full animated GIF editing use the Video to GIF tool.

Can I enlarge a small image without it looking blurry?

Not really. Enlarging invents pixels by interpolation and adds no real detail. Up to about 150 percent is usually acceptable on screen; beyond 200 percent softness is obvious. Machine-learning upscalers guess plausible detail, which is unreliable for text and faces.

What size should web images be?

Roughly twice the CSS width they will be displayed at, to cover 2× density screens. Going beyond that adds bytes with no visible benefit at normal viewing distances.