MSU Deinterlacer Benchmark — selecting the best deinterlacing filter
The most comprehensive comparison of deinterlacing methods
Dmitriy Konovalchuk
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
- 06.03.2024 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
We appreciate new ideas. Please, write us an e-mail to deinterlacer-benchmark@videoprocessing.ai
Leaderboard
Double framerate means that one frame is created from one field. In normal framerate, two fields are used for one frame.
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
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
-
MSU Benchmark Collection
- Video Saliency Prediction Benchmark
- LEHA-CVQAD Video Quality Metrics Benchmark
- Learning-Based Image Compression Benchmark
- Super-Resolution for Video Compression Benchmark
- Video Colorization Benchmark
- Defenses for Image Quality Metrics Benchmark
- Super-Resolution Quality Metrics Benchmark
- Deinterlacer Benchmark
- Metrics Robustness Benchmark
- Video Upscalers Benchmark
- Video Deblurring Benchmark
- Video Frame Interpolation Benchmark
- HDR Video Reconstruction Benchmark
- No-Reference Video Quality Metrics Benchmark
- Full-Reference Video Quality Metrics Benchmark
- Video Alignment and Retrieval Benchmark
- Mobile Video Codecs Benchmark
- Video Super-Resolution Benchmark
- Shot Boundary Detection Benchmark
- The VideoMatting Project
- Video Completion
- Codecs Comparisons & Optimization
- VQMT
- MSU Datasets Collection
- Metrics Research
- Video Quality Measurement Tool 3D
- Video Filters
- Other Projects