GPU-Accelerated Image Processing with WebGL
Using fragment shaders to apply complex image filters at GPU speeds in the browser.
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Elena Rostova
Contributing Author · Squoosh Next BlogWebGL fragment shaders execute on the GPU for every pixel in a texture simultaneously, making them orders of magnitude faster than equivalent Canvas ImageData loops for large images. A simple brightness-contrast shader runs at full resolution in under 1ms even on mobile GPUs, while a Canvas loop for the same operation on a 12-megapixel image may take 200ms. Implementing a shader pipeline requires creating a WebGL context, uploading the image as a texture, writing a GLSL fragment shader that performs the desired pixel operation, and rendering the result to an offscreen framebuffer.
The output can be read back to CPU memory using readPixels for further processing or encoding. The primary trade-off is implementation complexity — GLSL is a compiled language with strict typing, no dynamic allocation, and limited debugging tooling. For Squoosh Next's real-time preview, CSS filters are used for interactive adjustments while Canvas performs final destructive operations during export.
Key Takeaways
WebGL fragment shaders execute on the GPU for every pixel in a texture simultaneously, making them orders of magnitude faster than equivalent Canvas ImageData loops for large images.
A simple brightness-contrast shader runs at full resolution in under 1ms even on mobile GPUs, while a Canvas loop for the same operation on a 12-megapixel image may take 200ms.
Implementing a shader pipeline requires creating a WebGL context, uploading the image as a texture, writing a GLSL fragment shader that performs the desired pixel operation, and rendering the result to an offscreen framebuffer.
The output can be read back to CPU memory using readPixels for further processing or encoding.
Try It in the Workspace
Everything discussed in this article can be tested directly in Squoosh Next — no sign-up, no upload, 100% client-side.