nodeca/pica
Pica
Pica is a client-side utility for browser-based image scaling, format transformation, and file optimization. It functions as a canvas image processor that transforms data between canvas elements, bitmaps, and blobs for local manipulation within the web browser.
The library distinguishes itself through high-quality downscaling using Lanczos resampling and a multi-threaded processing model. It utilizes web workers to split image processing into tiles across CPU cores to increase resizing speed.
The project covers a broad range of image manipulation capabilities, including high-quality scaling and unsharp mask filtering for edge enhancement. It supports multiple input formats and provides utilities for exporting processed data into binary blob objects. To handle large images without causing browser crashes, the library employs chunked memory management and tiled processing.
Features
- Browser-Based Image Processing - Performs complex image scaling, transformations, and optimizations directly in the web browser.
- Parallel Tiling - Splits images into a grid of tiles to facilitate parallel execution across multiple CPU cores.
- Canvas-Based Image Processors - Transforms image data between canvas elements, bitmaps, and blobs for local browser-based manipulation.
- Client-Side Image Processing - Optimizes images by reducing dimensions and applying sharpening filters locally before upload.
- High-Quality Downscaling - Employs Lanczos resampling and sharpening filters to reduce image dimensions while maintaining high visual quality.
- Image Scaling - Scales images from source canvases or bitmaps to destination canvases using high-quality filters.
- Pixel Manipulators - Provides low-level manipulation of raw pixel data extracted from the browser canvas for scaling and filtering.
- Web Worker Parallelism - Distributes image processing tasks across multiple CPU cores using background web worker threads.
- Parallel Image Transformers - Utilizes web workers to distribute image processing tasks into tiles across multiple CPU cores to maximize resizing speed.
- Image Resizing - Scales image dimensions within the browser to reduce file size and bandwidth before server upload.
- Binary Buffer Management - Utilizes typed arrays and binary buffers for high-performance access to raw bitmap data.
- Unsharp Masking - Implements unsharp mask filters with adjustable parameters to enhance image edges and remove blur.
- Sharpening Filters - Provides an unsharp mask filter with adjustable parameters to enhance high-frequency components and remove blur from images.
- Chunked Processing - Handles large images in smaller segments to ensure stability on devices with limited memory.
- Binary Buffer Resizing - Processes raw bitmap data in typed arrays to produce a resized binary buffer.
- Browser-Based Large Image Handling - Processes very large images in chunks and parallel workers to prevent browser memory exhaustion and crashes.
- Chunked Memory Management - Implements segment-based processing of large images to prevent browser crashes and reduce memory peaks.
- Image Processing - Resizes images with high-quality Lanczos filtering.
- Image Processing - High-quality image resizing using Lanczos filters.
Star history
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