Video Quality Measurement Tool 3D

More than 100 blu-ray discs were tested

Projects, ideas: Dr. Dmitriy Vatolin
Implementation: 
Alexander Voronov, Denis Sumin, Marat Arsaev, Vyacheslav Napadovsky, Alexander Bokov, Alexey Fedorov, Alexander Belous, Alexey Shalpegin, Vladimir Yanushkovsky, Sergey Lavrushkin
In cooperation with IITP RAS: Prof. Galina Rozhkova

Introduction

VQMT3D (Video Quality Measurement Tool 3D) project was created to improve stereoscopic films. Our aim is to help filmmakers produce high-quality 3D video by finding inexpensive ways to automatically improve 3D film quality. Technical errors made during production of stereo 3D movies are often neglected, but according to our experiments these errors cause viewers to experience headaches.

I will find it very interesting to go through your report in detail, film by film. I had always thought that a major factor holding back the greater success of stereo 3D cinema could be technical problems like those your group has enumerated.

John Meritt, Senior Consulting Scientist at The Merritt Group; Founding Chair of the Stereoscopic Displays and Applications Conference

People stop watching 3D movies after experiencing pain just once. Therefore we focus on finding and fixing technical problems that could potentially cause headaches. These are the key contributions of the project:

#1 metric collection for stereo quality assessment

We have developed the largest set of metrics that detects technical problems in stereoscopic movies.

#1 stereoscopic movie quality evaluation

A large-scale evaluation of full-length 3D Blu-ray discs with detailed visualizations of technical errors.

#1 study of stereoscopic error influence on viewers

The largest study of reaction to stereoscopic errors. The collected data is crucial for research on visual fatigue.

List of Metrics

Our laboratory has been researching stereo quality and stereo artefacts that cause headaches for 11 years. During this time about 20 quality metrics have been created, and some metrics were significantly improved. For example, the metric for detecting swapped channels has gone through 3 generations of improvement (see channel mismatch metric), each time significantly improving accuracy. At the same time, the computational efficiency of the developed metrics remained better than those of our colleagues, which allowed us to actively use them to analyze real movies.

Metric Class Type Applicable to
1. Horizontal disparity Standard Measurable Any content
2. Vertical disparity Standard Measurable Any content
3. Scale mismatch Standard Measurable Any content
4. Rotation mismatch Standard Measurable Any content
5. Color mismatch Standard Measurable Any content
6. Sharpness mismatch Advanced Measurable Native 3D capture
7. Stereo window violation Advanced Measurable Any content
8. Crosstalk noticeability Advanced Measurable Any content
9. Depth continuity Advanced Measurable Any content
10. Cardboard effect Advanced Qualitative 2D-to-3D conversion
11. Edge-sharpness mismatch Unique Qualitative 2D-to-3D conversion
12. Channel mismatch Unique Qualitative Any content
13. Temporal asynchrony Unique Measurable Native 3D capture
14. Stuck-to-background objects Unique Qualitative 2D-to-3D conversion
15. Classification by production type Unique Qualitative Any content
16. Comparison with the 2D version Unique Qualitative 2D-to-3D conversion
17. Perspective distortions Unique Qualitative Any content
18. Converged axes Unique Qualitative Any content

Reports overview

Many stereographers have asked us, for example, if the MSU Scale Mismatch metric value of 4% is high or low for a movie. Originally, we didn’t have a clear answer. So we made a very good attempt to directly connect the values of metrics with perceived discomfort, making, perhaps, the most large-scale study on fragments of real movies with artifacts.

Then we tried to approach this problem from another side. With support from Intel, Cisco and Verizon, we bought more than 150 Blu-ray 3D movies and ran them all through our metrics. This project was complex on both the technical and organizational sides, but as a result we got a clear picture of how different metrics depend on the release date of a movie, its budget and production technology.

Blu-rays used in the reports
Report 1 (S3D shooting quality analysis of 5 movies) Download
(Additional info for bloggers and press)
Pages: 246
Figures: 295
Report 2 (S3D shooting quality analysis of 5 movies) Download
(Additional info for bloggers and press)
Pages: 342
Figures: 442
Report 3 (2D-3D conversion quality analysis of 5 movies) Download
(Additional info for bloggers and press)
Pages: 305
Figures: 336
Report 4 (S3D shooting quality analysis of 5 movies) Download Pages: 301
Figures: 402
Report 5 (2D-3D conversion quality analysis of 5 movies) Download
(Additional info for bloggers and press)
Pages: 384
Figures: 404
Report 6 (Stereo Window analysis of 10 movies) Download
(Additional info for bloggers and press)
Pages: 415
Figures: 455
Report 7 (Stereo Window analysis of 10 movies) Download
(Additional info for bloggers and press)
Pages: 333
Figures: 348
Report 8 (Rotate Analysis, Temporal Shift, Channels Swap, Zoom Mismatch in 25 movies) Download
(Additional info for bloggers and press)
Pages: 366
Figures: 361
Report 9 (Temporal Shift, Stuck-to-Background Objects, 2D to S3D conversion in Captured Films) Download
(Additional info for bloggers and press)
Pages: 467
Figures: 529
Report 10 (Overall analysis of 105 movies) Download Pages: 211
Figures: 270

Report 11 (Overall analysis of 10 selected Chinese movies)

Approximate release date: 2021

Viewer discomfort study

We have conducted a study to determine the amount of pain caused to the spectator by each of the stereoscopic error types. The data obtained from our experiment is the largest among similar experiments worldwide.

The experiment process

Passive stereoscopic glasses

University #subjects #subjects per video Duration #videos #videos per test #scores Year 3D tech. Stimuli
University of Surrey 30 30 25 40 40 1200 2010 Autostereo. Synthetic S3D seq., encoding with different QP
Catholic University of Korea 20 20 18 36 36 720 2011 Passive Captured S3D seq., different parallax and motion
Telecom Innovation Labs 24 24 50 64 64 1536 2011 Active Open S3D DB, different parallax
Philips Research Labs 24 24 24 7 7 168 2011 Autostereo. 3D movie (converted), different parallax
Beijing Institute of Ophthal. 30 30 30 1 1 30 2012 Active+passive 3D movie (captured)
LUNAM University 29 29 28 110 110 3190 2012 Active Open S3D DB (NAMA3DS1-COSPAD1),
different degradations
Yonsei University 28 28 29 110 110 3080 2013 Passive Open S3D DB,10 degradation types
Acreo Institute 48 28 29 110 110 5280 2013 Passive Open S3D DB,10 degradation types
Tampere University of Tech. 10 10 45 40 40 400 2013 Passive Captured S3D seq., encoding with different QP
Roma Tre University 854 43-255 90 - 1 854 2014 Passive Screening after watching S3D movies in cinemas
Yonsei University 56 56 10.5 30.1 31 1736 2014 Autostereo. Open S3D DB, different SI and TI
University College Dublin 40 4 5.5 25.11 33 1320 2015 Active Open S3D DB, packet losses
University of British Columbia 88 88 48 208 208 18304 2015 Passive Open S3D DB, different SI/TI and compression degradations
University North 146 18 8 184 26 146 2016 Active Open S3D DB, 22 degradation types
(packet losses, encoding, resizing,
disparity, brightness, geometry etc.)
University of Coimbra 35 2-6 7-1 184 26 146 2016 Active Open S3D DB, 22 degradation types
(packet losses, encoding, resizing,
disparity, brightness, geometry etc.)
Lomonosov MSU 302 370 40 60 60 22200 2017 Passive Scenes from 3D movies (captured), 4 types of S3D distortions,
5 intensities

Visualization of stereoscopic errors

Our visualizations distinctly demonstrate the stereoscopic errors. The reports of movie analysis include these visualizations.


An example of detected strong horizontal disparity from A Very Harold & Kumar 3D Christmas

An example of detected vertical disparity from Step Up Revolution

An example of detected color mismatch from The Amazing Spiderman

An example of detected sharpness mismatch from Alice in Wonderland

Publications

If you want to make a reference to this project, please refer to one of the following publications:

  1. Mikhail Erofeev, Dmitriy Vatolin, Alexander Voronov, Alexey Fedorov,
    “Toward an Objective Stereo-Video Quality Metric: Depth Perception of Textured Areas,”
    International Conference on 3D Imaging,
    pp. 1–6, 2012. doi:10.1109/IC3D.2012.6615120
  2. Dmitriy Akimov, Alexey Shestov, Alexander Voronov, Dmitriy Vatolin,
    “Automatic Left-Right Channel Swap Detection,”
    International Conference on 3D Imaging,
    pp. 1–6, 2012. doi:10.1109/IC3D.2012.6615126
  3. Alexander Voronov, Alexey Borisov, Dmitriy Vatolin,
    “System for automatic detection of distorted scenes in stereo video,”
    International Workshop on Video Processing and Quality Metrics for Consumer Electronic (VPQM-2012),
    pp. 138–143, 2012.
  4. Alexander Voronov, Dmitriy Vatolin, Denis Sumin, Vyacheslav Napadovsky, Alexey Borisov,
    “Towards Automatic Stereo-video Quality Assessment and Detection of Color and Sharpness Mismatch,”
    International Conference on 3D Imaging,
    pp. 1–6, 2012. doi:10.1109/IC3D.2012.6615121
  5. Alexander Voronov, Dmitriy Vatolin, Denis Sumin, Vyacheslav Napadovsky, Alexey Borisov,
    “Methodology for stereoscopic motion-picture quality assessment,”
    Proc. SPIE 8648, Stereoscopic Displays and Applications XXIV,
    vol. 8648, pp. 864810-1–864810-14, 2013. doi:10.1117/12.2008485
  6. Alexander Bokov, Dmitriy Vatolin, Anton Zachesov, Alexander Belous, Mikhail Erofeev,
    “Automatic detection of artifacts in converted S3D video,”
    Proc. SPIE 9011, Stereoscopic Displays and Applications XXV (March 6, 2014),
    vol. 901112, pp. 1–14, 2014. doi:10.1117/12.2054330
  7. Stanislav Dolganov, Mikhail Erofeev, Dmitriy Vatolin, Yury Gitman,
    “Detection of stuck-to-background objects in converted S3D movies,”
    2015 International Conference on 3D Imaging, IC3D 2015,
    pp. 1–6, 2015. doi:10.1109/IC3D.2015.7391839
  8. Yury Gitman, Can Bal, Mikhail Erofeev, Ankit Jain, Sergey Matyunin, Kyoung-Rok Lee, Alexander Voronov, Jason Juang, Dmitriy Vatolin, Truong Nguyen,
    “Delivering Enhanced 3D Video,”
    Intel Technology Journal,
    vol. 19, pp. 162–200, 2015.
  9. Dmitriy Vatolin, Alexander Bokov, Mikhail Erofeev, Vyacheslav Napadovsky,
    “Trends in S3D-Movie Quality Evaluated on 105 Films Using 10 Metrics,”
    Proceedings of Stereoscopic Displays and Applications XXVII,
    pp. SDA-439.1–SDA-439.10, 2016. doi:10.2352/ISSN.2470-1173.2016.5.SDA-439
  10. Alexander Bokov, Sergey Lavrushkin, Mikhail Erofeev, Dmitriy Vatolin, Alexey Fedorov,
    “Toward fully automatic channel-mismatch detection and discomfort prediction for S3D video,”
    2016 International Conference on 3D Imaging (IC3D),
    pp. 1–7, 2016. doi:10.1109/IC3D.2016.7823462
  11. Dmitriy Vatolin, Sergey Lavrushkin,
    “Investigating and predicting the perceptibility protect of channel mismatch in stereoscopic video,”
    Moscow University Computational Mathematics and Cybernetics,
    vol. 40, pp. 185–191, 2016. doi:10.3103/s0278641916040075
  12. Anastasia Antsiferova, Dmitriy Vatolin,
    “The influence of 3D video artifacts on discomfort of 302 viewers,”
    2017 International Conference on 3D Immersion (IC3D),
    pp. 1–8, 2017. doi:10.1109/IC3D.2017.8251897
  13. Dmitriy Vatolin, Alexander Bokov,
    “Sharpness Mismatch and 6 Other Stereoscopic Artifacts Measured on 10 Chinese S3D Movies,”
    Proceedings of Stereoscopic Displays and Applications XXVIII,
    pp. 137–144, 2017. doi:10.2352/ISSN.2470-1173.2017.5.SDA-340
  14. Sergey Lavrushkin, Vitaliy Lyudvichenko, Dmitriy Vatolin,
    “Local Method of Color-Difference Correction Between Stereoscopic-Video Views,”
    Proceedings of the 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON),
    pp. 1–4, 2018. doi:10.1109/3DTV.2018.8478453
  15. Aidar Khatiullin, Mikhail Erofeev, Dmitriy Vatolin,
    “Fast Occlusion Filling Method For Multiview Video Generation,”
    Proceedings of the 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON),
    pp. 1–4, 2018. doi:10.1109/3DTV.2018.8478562
  16. Sergey Lavrushkin, Dmitriy Vatolin,
    “Channel-Mismatch Detection Algorithm for Stereoscopic Video Using Convolutional Neural Network,”
    Proceedings of the 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON),
    pp. 1–4, 2018. doi:10.1109/3DTV.2018.8478542
  17. Alexander Ploshkin, Dmitriy Vatolin,
    “Accurate Method of Temporal Shift Estimation for 3D Video,”
    Proceedings of the 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON),
    pp. 1–4, 2018. doi:10.1109/3DTV.2018.8478431

Acknowledgments

We wish to acknowledge the help provided by CMC Faculty of Lomonosov Moscow State University.
CMC Faculty provided us with extra computational capabilities and disk space which was needed for our research.

This work is partially supported by the Intel/Cisco Video Aware Wireless Network (VAWN) Program and by grant 10-01-00697a from the Russian Foundation for Basic Research.

Our project “Development of a system for automatic objective quality assessment and correction of stereoscopic video and video in VR180 format” was supported under the START program of State Fund for Support of Small Enterprises in the Scientific-Technical Fields.

Invitation to the Project

We invite stereographers, researchers and proofreaders to join our 3D-film analysis project. We are open for collaboration and appreciate your ideas and contributions. We love to receive feedback and learn from the experience of people in the film-production industry.

If you would like to participate, please contact us: 3dmovietest@graphics.cs.msu.ru

Contacts

For questions and propositions, please contact us: 3dmovietest@graphics.cs.msu.ru

MSU 3D-video Quality Analysis. Report 10
Detection of stereo window violation
How to find objects that are present only in one view?
Depth continuity estimation in S3D video
How smooth is the depth transition between scenes?
Detection of 3D movie scenes shot on converged axes
Another cause of headaches when watching 3D movies.
Parallax range estimation in S3D video
The parallax range should be both comfortable and entertaining for spectators.
Geometric distortions analysis and correction
Automatic correction of vertical disparity, rotation mismatch and scale mismatch.
Automatic detection of artifacts in converted S3D videos
We detect edge sharpness mismatch, cardboard effect, and crosstalk noticeability.
Temporal shift estimation for stereoscopic videos
How to take into account geometric distortions in the estimation of the temporal shift?
Neural network-based algorithm for classification of stereoscopic video by the production method
What method was used to create the 3D scene?
Perspective distortions estimation
How to detect a mismatch in the vertical position of the cameras?
Method for region of interest selection with noticeable stereoscopic distortions in S3D videos
How to find foreground objects that are stuck to the background?
Forecasting of viewers’ discomfort
How do distortions in a stereo movie affect the discomfort of viewers?
Automatic detection and analysis of techniques for 2D to 3D video conversion
What conversion methods were used and how much the original frames are distorted?
Automatic color mismatch estimation in S3D videos using confidence maps
How to detect color distortion between the angles of a 3D video?
Detection of object boundary inconsistencies between 2D-3D conversion results and depth maps
How to find foreground objects that are stuck to the background?
Automatic local color correction in S3D video
How to eliminate color distortion between stereo video views?
Detection of swapped views in S3D movies
Channel mismatch is hard to detect, but our neural network method shows very high precision.
Automatic sharpness mismatch detection and compensation in stereo
While watching movies with sharpness mismatch the spectator may lose sense of 3D or even get a headache.
MSU 3D-video Quality Analysis. Report 9
MSU 3D-video Quality Analysis. Report 9 press release
MSU 3D-video Quality Analysis. Report 8
MSU 3D-video Quality Analysis. Report 8 press release
MSU 3D-video Quality Analysis. Report 7
MSU 3D-video Quality Analysis. Report 7 press release
MSU 3D-video Quality Analysis. Report 6
MSU 3D-video Quality Analysis. Report 6 press release
MSU 3D-video Quality Analysis. Report 5
MSU 3D-video Quality Analysis. Report 5 press release
MSU 3D-video Quality Analysis. Report 4
MSU 3D-video Quality Analysis. Report 3
MSU 3D-video Quality Analysis. Report 3 press release
MSU 3D-video Quality Analysis. Report 2
MSU 3D-video Quality Analysis. Report 2 press release
MSU 3D-video Quality Analysis. Report 1
MSU 3D-video Quality Analysis. Report 1 press release
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3D Device Testing: Tested devices
MSU 3D Display Video Capture (video capture for glasses-free 3D display)
Our laboratory was challenged to make a 3d film using cheap widespread devices - web-cameras.
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