---
id: sk-video
version: "1.1.10"
license: BSD
license_treatment: permissive
maintenance: active
---

# sk-video — Video Processing in Python

License: permissive · Maintenance: active · Downloads: 305.2K/mo

## What it is and what it does

This package provides video I/O and analysis capabilities, wrapping FFmpeg to read and write video frames, extract metadata, and compute video quality metrics. It follows the conventions of scikit-image and scikit-learn, sitting on top of numpy and scipy for workflows already using the scientific Python stack.

The library provides frame-by-frame video access, metadata inspection, and implementations of standard quality metrics (MSE, PSNR, SSIM, MS-SSIM, NIQE, BRISQUE, VIIDEO, Video-BLIINDS, ST-RRED). Recent versions validated metric implementations against reference code and fixed divergences; the 1.1.12+ line added pathlib.Path support, URL and file-like object I/O, and audio passthrough. The project is actively maintained with ongoing compatibility work for modern NumPy and Python versions.

Use it for:

- Extract frames from video files and process them with numpy and scipy for computer vision tasks.
- Compute video quality metrics (SSIM, PSNR, NIQE, BRISQUE) for compression or enhancement evaluation.
- Read video metadata and properties without decoding all frames.
- Batch process video files to generate frame sequences for machine learning pipelines.
- Validate video codec compatibility and inspect FFmpeg-supported container formats.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Reads, writes, and processes video files using FFmpeg as its backend, providing video I/O and quality metrics (NIQE, BRISQUE, SSIM, PSNR) built on numpy and scipy.

Yes, if you need video I/O and quality metrics in a Python environment already using numpy and scipy. Install friction is low and maintenance is active. Requires FFmpeg >= 2.8 on system PATH and Python >= 3.10. Metric implementations changed in 1.2.0 (more accurate but incomparable to 1.1.x scores); verify this does not break existing workflows. No known security vulnerabilities.

## Install

pip install sk-video
uv add sk-video
poetry add sk-video

## Installing sk-video

Before you install: Low install friction; pure Python wheel with only numpy and scipy as runtime dependencies. Actively maintained with recent metric accuracy fixes and NumPy 2.x compatibility. Requires FFmpeg >= 2.8 on system PATH and Python >= 3.10.

License in practice: BSD permissive license allows commercial and private use with minimal restrictions; attribution and license notice required in distributions.

Quickstart:

pip install scikit-video

import skvideo.io
video_data = skvideo.io.vread('video.mp4')
metadata = skvideo.io.ffprobe('video.mp4')

FFmpeg >= 2.8 must be installed and on system PATH; Python >= 3.10 required.

Verify before relying:

- Whether the metric accuracy changes in 1.2.0 (NIQE, BRISQUE, VIIDEO, Video-BLIINDS) affect existing workflows or benchmarks.
- Current state of the deprecated libav/avconv backend and mediainfo support (planned removal in 1.4.0).
- Whether Pillow >= 9.0 is a runtime or optional dependency.

## Package facts

- License: BSD (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 305.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

video processing python, read write video files, video quality metrics, ffmpeg python wrapper, video frame extraction, video analysis, video-processing, ffmpeg-wrapper, quality-metrics

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