BlurHash
BlurHash is an algorithm that creates a hash representation of an image which allows previewing that image as it loads, while using very little bandwidth. The hash is typically around 20-30 characters long.
image-compression
image-preview
low-bandwidth
BlurHash: Algorithm for Lazy Image Preview
BlurHash creates a hash representation of an image, allowing previewing as it loads with minimal bandwidth usage, typically resulting in a 20-30 character long hash.
What is BlurHash?
BlurHash is an open-source algorithm developed by Dag Ågren that generates compact representations of images that allow creating blurry placeholder previews. It works by capturing the basic colors and outlines of an image in a shorthand format. TheBlurHash for an image is typically around 20-30 characters long.
When the blurhash string is decoded, it renders a gradient blob that resembles the original image colors and shapes. This provides a preview of the image before it has fully loaded, which improves perceived performance. The small size of blurhashes also makes them suitable for use in thumbnail summaries and previews in listings.
BlurHash strikingly balances precision and size. At just 150-500 bytes, a blurhash can encode enough information to render a fuzzy preview image. The algorithm is flexible - longer hashes allow more complex and higher-fidelity blur effects. Blurhashes give developers control over this tradeoff between size and quality.
By providing lightweight image placeholders, BlurHash helps avoid layout reflows in web pages and applications as images load. It can also simplify handling of not-yet-loaded images across long lists and grids. The compact hashes are easy to generate server-side and integrate client-side to handle preview rendering efficiently.
BlurHash Features
Features
- Generates a short hash string to represent an image
- Allows previewing and blurring images before they are fully loaded
- Works with very small amounts of data - hashes are usually 20-30 characters
- Encodes color and geometric information about an image
- Open source algorithm released by Wolt under MIT license
Pricing
- Open Source
Pros
Dramatically improves perceived performance of loading images
Creates placeholder previews using very little data
Lightweight and fast to generate hashes on the server
Supported in many programming languages and frameworks
Cons
Not an exact preview - just an approximation of the image
More complex implementation compared to simple placeholders
Requires generating hashes on the server side
Official Links
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