Video Quality Measurement Tool 3D

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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.

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.

• 18 metrics in total
• 14 metrics, including 5 unique ones, provide quality estimation of 2D-to-3D conversion

#1 stereoscopic movie quality evaluation

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

• Over 100 analyzed movies
• 9 reports published for industry professionals
• 3,100 pages in total
• 100 stereographers were contacted, 32 of them contributed to the evaluation

#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.

• Over 300 people participated
• 22,200 discomfort scores acquired

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.

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)	Download	Pages: 322 Figures: 566
Report 12 (VR180 Quality Analysis)	Download	Pages: 348 Figures: 362

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.

Visualization of stereoscopic errors

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

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,
   2012. doi:10.1109/IC3D.2012.6615120 (download)
2. Dmitriy Akimov, Alexey Shestov, Alexander Voronov, Dmitriy Vatolin,
   “Automatic Left-Right Channel Swap Detection,”
   International Conference on 3D Imaging,
   2012. doi:10.1109/IC3D.2012.6615126 (download)
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. (download)
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,
   2012. doi:10.1109/IC3D.2012.6615121 (download)
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 (download)
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, 2014. doi:10.1117/12.2054330 (download)
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,
   2015. doi:10.1109/IC3D.2015.7391839 (download)
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. (download)
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 (download)
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),
    2016. doi:10.1109/IC3D.2016.7823462 (download)
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 (download)
12. Anastasia Antsiferova, Dmitriy Vatolin,
    “The influence of 3D video artifacts on discomfort of 302 viewers,”
    2017 International Conference on 3D Immersion (IC3D),
    2017. doi:10.1109/IC3D.2017.8251897 (download)
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 (download)
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),
    2018. doi:10.1109/3DTV.2018.8478453 (download)
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),
    2018. doi:10.1109/3DTV.2018.8478562 (download)
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),
    2018. doi:10.1109/3DTV.2018.8478542 (download)
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),
    2018. doi:10.1109/3DTV.2018.8478431 (download)
18. Sergey Lavrushkin, Konstantin Kozhemyakov, Dmitriy Vatolin,
    “Neural-Network-Based Detection Methods for Color, Sharpness, and Geometry Artifacts in Stereoscopic and VR180 Videos,”
    International Conference on 3D Immersion (IC3D),
    2020. doi:10.1109/IC3D51119.2020.9376385 (download)
19. Kirill Malyshev, Sergey Lavrushkin, Dmitriy Vatolin,
    “Stereoscopic Dataset from A Video Game: Detecting Converged Axes and Perspective Distortions in S3D Videos,”
    International Conference on 3D Immersion (IC3D),
    2020. doi:10.1109/IC3D51119.2020.9376375 (download)
20. Lavrushkin Sergey, Molodetskikh Ivan, Kozhemyakov Konstantin, Vatolin Dmitriy,
    “Stereoscopic quality assessment of 1,000 VR180 videos using 8 metrics,”
    Electronic Imaging, 3D Measurement and Data Processing,
    2021. doi:10.2352/issn.2470-1173.2021.2.sda-350 (download)

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