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Netflix/vmaf

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Vmaf

This project is a video quality assessment library and suite of tools designed to quantify video degradation and identify banding artifacts. It provides a perceptual video quality metric that compares distorted video streams against a high-quality reference to estimate human visual perception.

The toolkit includes a specialized system for training and validating custom perceptual quality models using specific datasets. It also features a contrast-aware multiscale index specifically for detecting contouring and banding artifacts in video streams.

The library covers objective video metric calculation and video compression analysis, utilizing both traditional mathematical measures and fusion-based algorithms. These capabilities allow for the evaluation of different encoders or bitrates and the identification of specific visual distortions.

The implementation consists of a core library written in C with a Python binding wrapper for high-level analysis and model training.

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Features

  • Perceptual Quality Metrics - Implements perceptual quality metrics like VMAF to measure the visual degradation of video streams.
  • C Image Processing Libraries - Provides a high-performance C core for pixel-level processing and video quality metric calculations.
  • Banding Detection - A contrast-aware multiscale index for identifying contouring and banding artifacts in video streams.
  • Metric Fusion Algorithms - Implements fusion-based aggregation to combine various low-level metrics into a single human-perceptual quality score.
  • Reference-Based Quality Comparisons - Calculates quality loss by comparing distorted video frames against a high-quality original reference source.
  • Video Artifact Detection - Identifies specific visual distortions such as banding and contouring to ensure professional video quality.
  • Video Quality Metrics - Computes standard mathematical measurements of video degradation to provide consistent objective quality scores.
  • Python-C Interfaces - Provides a Python interface that wraps the C core for high-level video analysis and model training.
  • Model Training Toolkits - Includes a toolkit for developing and validating custom perceptual quality models using training datasets.
  • Quality - Enables the development and validation of specialized video assessment models tailored to specific viewing conditions.
  • Contrast Analysis - Employs multiscale contrast analysis to detect blurring and banding artifacts across different spatial resolutions.
  • Compression Impact Analysis - Evaluates the impact of different encoders and bitrates on visual quality to optimize streaming efficiency.
  • Ads and Analytics - Perceptual video quality assessment library based on multi-method fusion.
  • Monitoring and QC - Perceptual video quality assessment tool.
5,387 stars·822 forks·C·29 views

Star history

Star history chart for netflix/vmafStar history chart for netflix/vmaf

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with Vmaf

These projects share indexed features with Vmaf. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • leandromoreira/digital_video_introductionleandromoreira avatar

    leandromoreira/digital_video_introduction

    16,232View on GitHub↗

    This project is an educational suite and technical guide designed for mastering video codecs and signal processing. It provides a structured curriculum through an engineering course, interactive labs, and tutorials focused on the fundamental principles of video compression and digital signal processing. The resource includes a technical guide for analyzing specific codecs like AV1, VP9, and H.265. It distinguishes itself by providing a containerized media lab, which ensures a consistent development environment for experimenting with video technology tools and notebooks. The project covers a

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  • marcantoine-arnaud/wisualMarcAntoine-Arnaud avatar

    MarcAntoine-Arnaud/wisual

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    Web Service for Visual Quality Assessment

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  • bavc/qctoolsbavc avatar

    bavc/qctools

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    QCTools (Quality Control Tools for Video Preservation) is a free and open source software tool that helps users analyze and understand their digitized video files through use of audiovisual analytics and filtering. QCTools is funded by the National Endowment for the Humanities and the Knight…

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  • pistondevelopers/imagePistonDevelopers avatar

    PistonDevelopers/image

    5,805View on GitHub↗

    This project is an image processing library designed for manipulating pixel data, applying visual filters, and adjusting image appearance. It functions as a low-level tool for processing raw pixel buffers and managing image data. The library provides a toolkit for image geometry manipulation, including resizing, cropping, flipping, and rotating images. It also serves as an image format converter for encoding and decoding data between common binary file formats and as a metadata extractor for retrieving dimensions and color profiles from file headers. The capabilities cover low-level image pr

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Frequently asked questions

What does netflix/vmaf do?

This project is a video quality assessment library and suite of tools designed to quantify video degradation and identify banding artifacts. It provides a perceptual video quality metric that compares distorted video streams against a high-quality reference to estimate human visual perception.

What are the main features of netflix/vmaf?

The main features of netflix/vmaf are: Perceptual Quality Metrics, C Image Processing Libraries, Banding Detection, Metric Fusion Algorithms, Reference-Based Quality Comparisons, Video Artifact Detection, Video Quality Metrics, Python-C Interfaces.

Which projects share features with netflix/vmaf?

Projects with overlapping indexed features include: leandromoreira/digital_video_introduction — This project is an educational suite and technical guide designed for mastering video codecs and signal processing. It… marcantoine-arnaud/wisual — Web Service for Visual Quality Assessment. bavc/qctools — QCTools (Quality Control Tools for Video Preservation) is a free and open source software tool that helps users… pistondevelopers/image — This project is an image processing library designed for manipulating pixel data, applying visual filters, and… numpy/numpy — NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing… lava/matplotlib-cpp — matplotlib-cpp is a header-only C++ library and wrapper that enables the creation of 2D and 3D visualizations by…