Streaming image processing allows you to work with large or partially available image data without loading the entire file into memory. Pillow’s ImageFile.Parser and incremental decode methods enable efficient, on-the-fly processing—ideal for large JPEGs, network streams, or real-time applications.

Processing high-resolution or gigapixel images can quickly exhaust system memory if loaded entirely into RAM. This guide presents techniques—such as streaming, chunked processing, block allocator tuning, and Pillow-SIMD optimizations—to efficiently handle large images in Python using Pillow.

Extending Pillow‘s ImageFilter module with custom filters unlocks powerful image processing capabilities—ranging from edge detection to color grading via 3D lookup tables. This guide covers building convolution kernels, 3D LUT transforms, and integrating NumPy for per-pixel operations, with performance tips like Pillow-SIMD and multi-threading.