squish: Compress and Convert Images in Your Browser

Overview

Squish is a browser-based image compression and conversion tool. It addresses the friction of optimizing images one at a time by letting users process a batch, adjust quality, preview results, and download the output from a single interface.

Compression runs in the browser using WebAssembly codecs, so the workflow does not require server uploads. It is a practical fit for designers, developers, and content creators who need to optimize files before publishing or sharing them.

Key Features

• Compress images in batches with a queue for larger sets of files.
• Convert between AVIF, JPEG, JPEG XL, PNG, and WebP.
• Set compression quality for supported formats.
• Preview images and review size reduction statistics.
• Add images by dragging and dropping or selecting files.
• Download individual results or use the batch download option.

Use Cases

• A web developer can convert and shrink assets before adding them to a website.
• A designer can compare previews and quality settings when preparing image exports.
• A content creator can optimize a group of images before uploading them to a publishing platform.
• A privacy-conscious user can compress local files without sending them to a remote service.

Project Facts

• Language: TypeScript
• License: MIT
• Stars: 1.1k
• Forks: 114
• Topics: batch, batch-processing, image-compression, image-processing
• Archived: No

Getting Started

Requires Node.js 18 or later and npm 7 or later.

git clone https://github.com/addyosmani/squish.git
cd squish && npm install && npm run dev

See the README for production build instructions and usage details. Try the hosted app at squish.addy.ie.

Alternatives

• pic-smaller: Pic Smaller also compresses and converts image batches locally in a browser, with a similarly focused workflow rather than a distinct editing task.

Considerations

• Processing is browser-based, so performance and supported formats can depend on the user’s browser and device.
• Batch processing can use substantial device resources for large image sets.
• The project requires a modern Node.js and npm environment for local development.