Main page — MSU Benchmark Collection

MSU Deinterlacer Benchmark — selecting the best deinterlacing filter

The most comprehensive comparison of deinterlacing methods

Powered by
Powered by
G&M Lab head: Dr. Dmitriy Vatolin
Measurements, analysis: 
Alexey Zelentsov,
Dmitriy Konovalchuk
Maintainer: 
Andrey Sulema

Key features of the Benchmark

  • For Deinterlacing methods’ users
    • Choose deinterlacing method that is the best for your speed and quality requirements
    • Discover the newest deinterlacing methods’ achievements
  • For Researchers and Developers
    • Quickly get comprehensive comparison results for your paper with our tables, visual comparison tools and performance plots
    • Check the performance of your deinterlacing method on the complex cases

What’s new

  • 06.05.2024 Added subjective scores for FLAD, SwinDI. Updated visualization and metrics result
  • 11.08.2022 Added FLAD
  • 16.02.2022 Added SwinDI
  • 24.11.2021 Added subjective scores for EDVR, EDVR_toWSA, TDAN, DUF, ST-Deint, new versions of DfRes and MFDIN
  • 04.11.2021 Added ST-Deint
  • 13.10.2021 Added EDVR, EDVR_toWSA, TDAN, DUF, new versions of DfRes and MFDIN. New Leader! MFDIN L Deinterlacer
  • 06.10.2021 Added subjective comparison results (MOS)
  • 22.09.2021 Added Sony Vegas Built-In
  • 17.09.2021 Added MFDIN, Adobe Premiere Pro Built-IN
  • 01.09.2021 New Leader! DfRes 122000 G2e 3 Deinterlacer
  • 07.07.2021 New 2021 Dataset
  • 22.12.2020 Added VS EEDI3, VS TDeintMod, MC Deinterlacer. Tuned Kernel Deinterlacer
  • 26.11.2020 Beta-version Release

To submit deinterlacing method, please, follow 3 simple steps in the the Deinterlacer Submission section

We appreciate new ideas. Please, write us an e-mail to deinterlacer-benchmark@videoprocessing.ai

Leaderboard

Сharts

Visualization

In this section you can see a frame, a crop from this frame, and also MSU VQMT PSNR Visualization of this crop.

Drag a red rectangle in the area which you want to crop

The frame to compare on:
Deinterlacer 1: Deinterlacer 2:

GT

VQMT PSNR Visualization

MFDIN L

DfRes SA

Deinterlacer Submission

To submit, you can either send us any executable file or code of your deinterlacer, or follow these 3 simple steps

1. Download the interlaced video here.
We have more available formats, if YUV is not suitable. Just click on this text
There are 3 available options:
    a. Download frames of the video sequence in .png format here
    b. Download .yuv video file generated from frames via

    ffmpeg -i %04d.png -c:v rawvideo -pix_fmt yuv444p sequences.yuv

    here
    c. Download lossless encoded .mkv video generated from frames via

    ffmpeg -i %04d.png -c:v libx264 -preset ultrafast -crf 0 -pix_fmt yuv444p lossless.mkv

    here

2. Deinterlace downloaded video
The details, which may help you
    TFF interlacing was used to get interlaced sequence from GT
    The video consists of 28 videos, each separated by 5 black frames. Black frames are ignored while measuring

3. Send us an email to deinterlacer-benchmark@videoprocessing.ai with the following information:
    A. Name of the deinterlacing method that will be specified in our benchmark
    B. Link to the cloud drive (Google Drive, OneDrive, Dropbox, etc.), containing deinterlaced video.
    C. (Optional) Any additional information about the method
    Click here to see what may be included in additional information
      Technical information about deinterlaced video (e.g. colorspace, file-type, codec)
      The name of the theoretical method used
      Full name of the deinterlacing method or product
      The version that was used
      The parameter set that was used
      Any other additional information
      A link to the code of your deinterlacing method, if it is open-source
      A link to the paper about your deinterlacing method
      A link to the documentation of your deinterlacing method. For example, this is suitable for deinterlacing methods that are implemented as a part of a video processing framework.
      A link to the page, where users can purchase or download your product (for example, VirtualDub Plugin)
    D. (Optional) If you would like us to tune the parameters of your deinterlacing method, you should give us an ability to
    launch it. You can do it by sending us a code or an executable file, providing us a free test version of your product, or any
    other possible way, that is convenient for you

Our policy:

  • We won't publish the results of your method without your permission.
  • We share only public samples of our dataset as it is private.

Contacts

For questions and propositions, please contact us: deinterlacer-benchmark@videoprocessing.ai

You can subscribe to updates on our benchmark:



MSU Video Quality Measurement Tool

              

    The tool for performing video/image quality analyses using reference or no-reference metrics

Widest Range of Metrics & Formats

  • Modern & Classical Metrics SSIM, MS-SSIM, PSNR, VMAF and 10+ more
  • Non-reference analysis & video characteristics
    Blurring, Blocking, Noise, Scene change detection, NIQE and more

Fastest Video Quality Measurement

  • GPU support
    Up to 11.7x faster calculation of metrics with GPU
  • Real-time measure
  • Unlimited file size

    • Main MSU VQMT page on compression.ru

Crowd-sourced subjective
quality evaluation platform

  • Conduct comparison of video codecs and/or encoding parameters

What is it?

Subjectify.us is a web platform for conducting fast crowd-sourced subjective comparisons.

The service is designed for the comparison of images, video, and sound processing methods.

Main features

  • Pairwise comparison
  • Detailed report
  • Providing all of the raw data
  • Filtering out answers from cheating respondents

    • Subjectify.us
05 Nov 2020
See Also
MSU Image- and video-quality metrics analysis
Description of a project in MSU Graphics and Media Laboratory
Learning-Based Image Compression Benchmark
Super-Resolution Quality Metrics Benchmark
Discover 66 Super-Resolution Quality Metrics and choose the most appropriate for your videos
Video Saliency Prediction Benchmark
Explore the best video saliency prediction (VSP) algorithms
Super-Resolution for Video Compression Benchmark
Learn about the best SR methods for compressed videos and choose the best model to use with your codec
Metrics Robustness Benchmark
Check your image or video quality metric for robustness to adversarial attacks
Site structure
MSU Graphics & Media Lab Video Group
2019–2022