scikit-video - Scientific Video Processing

scikit-video simplifies the complex world of video codecs and containers by providing a consistent NumPy-based interface. It allows for the calculation of motion vectors, video quality assessment (VQA), and seamless integration with the rest of the scientific Python stack.

When to Use

• Reading and writing video files in various formats (MP4, AVI, MKV) via FFmpeg.
• Extracting specific frames or segments from long videos without loading them entirely into memory.
• Calculating motion estimation (Block Matching, Optical Flow).
• Measuring video quality (PSNR, SSIM, VIF, NIQE).
• Generating video datasets for machine learning.
• Visualizing temporal changes in pixel data (e.g., scientific recordings).
• Handling raw YUV data streams.

Reference Documentation

Official docs: http://www.scikit-video.org/
GitHub: https://github.com/scikit-video/scikit-video
Search patterns: skvideo.io.vread, skvideo.io.FFmpegReader, skvideo.motion, skvideo.measure