+7 (495) 957-77-43
Контакты

T-Comm_Article 6_2_2022

Извините, этот техт доступен только в “Американский Английский”. For the sake of viewer convenience, the content is shown below in the alternative language. You may click the link to switch the active language.

ANALYSIS OF CURRENT VIDEO DATABASES FOR QUALITY ASSESSMENT

Anastasia Mozhaeva, The University of Waikato, Hamilton, New Zealand;
Moscow Technical University of Communications and Informatics Moscow, Russia, anast.mozhaeva@gmail.com
Elizaveta Vashenko, Moscow Technical University of Communications and Informatics Moscow, Russia
Vladimir Selivanov, Moscow Technical University of Communications and Informatics Moscow, Russia
Alexei Potashnikov, Moscow Technical University of Communications and Informatics Moscow, Russia
Igor Vlasuyk, Moscow Technical University of Communications and Informatics Moscow, Russia
Lee Streeter, The University of Waikato, Hamilton, New Zealand

Abstract
The popularity of video streaming has grown significantly over the past few years. Video quality prediction metrics can be used to perform extensive video codec analysis and customize high-quality assurance. Video databases with subjective ratings form an important basis for training video quality metrics, and codecs based on machine learning algorithms. More than three dozen subjective video databases are now available. In this article, modern video databases are presented, analyzed current database and findings methods for improving. For analysis, performance criteria are proposed based on subjective assessments when creating a database of video sequences. At this stage of development, subjective assessments are the most difficult part of creating a database of video sequences, since these assessments are expensive and time-consuming. In addition, subjective experimentation is further complicated by many factors, including viewing distance, a display device, lighting conditions, vision, and mood of the subjects. This information will allow researchers to have a more detailed understanding of the video databases, a new method for collecting subjective data, and can also help in planning future experiments.

Keywords: video quality assessment, subjective testing, video database, video dataset, applied television, mean opinion score (MOS).

References

  1. Cisco Visual Networking Index: Forecast and Methodology 2017- 2022, Feb. 2019, [online] Available: https://www.cisco.com/c/en/us/ solutions/collateral/serviceprovider/visual-networking-index-vni/white-paper-c11741490.html.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68-73.
  2. S.-F. Chang and A. Vetro (2005), «Video adaptation: Concepts technologies and open issues», Proc. of the IEEE, vol. 93, no. 1, pp. 148-158.
  3. A. Mozhaeva, L. Streeter, I. Vlasuyk and A. Potashnikov (2021), «Full Reference Video Quality Assessment Metric on Base Human Visual System Consistent with PSNR,» 2021 28th Conference of Open Innovations Association (FRUCT), pp. 309-315.
  4. A. I. Mozhaeva, I. V. Vlasuyk, A. M. Potashnikov, M. J. Cree and L. Streeter (2021), «The Method and Devices for Research the Parameters of the Human Visual System to Video Quality Assessment,» 2021 Systems of Signals Generating and Processing in the Field of on Board Communications, pp. 1-5.
  5. S. Winkler (2012), «Analysis of Public Image and Video Databases for Quality Assessment,» in IEEE Journal of Selected Topics in Signal Processing, vol. 6, no. 6, pp. 616-625, Oct. 2012.
  6. F. De Simone et al. (2009), “EPFL-PoliMI video quality assessment database,” [Online]. Available: http://vqa.como.polimi.it/
  7. F. De Simone et al. (2009), “Subjective assessment of H.264/AVC video sequences transmitted over a noisy channel,” in Proc. Int. Workshop Quality of Multimedia Experience (QoMEX), San Diego, CA, Jul. 29-31.
  8. S. Péchard, R. Pépion, and P. Le Callet, “IRCCyN IVC 1080i data-base,” 2008 [Online]. Available: http://www.irccyn.ec-nantes.fr/spip. php?article541.
  9. S. Péchard, R. Pépion, and P. Le Callet (2008), “Suitable methodology in sub- jective video quality assessment: A resolution dependent paradigm,” in Proc. Int. Workshop Image Media Quality and its Applicat. (IMQA), Kyoto, Japan, Sep. 2008.
  10. F. Boulos, W. Chen, B. Parrein, and P. Le Callet, “IRCCyN IVC SD RoI database,” 2009 [Online]. Available: http://www.ir-ccyn.ec-nantes.fr/spip.php?article551.
  11. S. Péchard, R. Pépion, and P. Le Callet (2000), “Region-of-interest intra prediction for H.264/AVC error resilience,” in Proc. Int. Conf. Image Process. (ICIP), Cairo, Nov. 7-10.
  12. F. Zhang, S. Li, L. Ma, Y. C. Wong, and K. N. Ngan (2011), “IVP subjective quality video database,” [Online]. Available: http://ivp.ee.cuhk. edu.hk/research/database/subjective/
  13. K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack (2010), “LIVE video quality database,” [Online]. Available: http://live. ece.utexas.edu/research/quality/live_video.html
  14. K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack (2010), “Study of subjective and objective quality assessment of video,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1427-1441, Jun. 2010.
  15. L. Goldmann et al. (2010), “3D video quality assessment,” [Online]. Available: http://mmspl.epfl.ch/page38842.html.
  16. L. Goldmann, F. De Simone, and T. Ebrahimi (2010), “A comprehensive data- base and subjective evaluation methodology for quality of experience in stereoscopic video,” in Proc. SPIE 3D Image Process. (3DIP) and Applicat., San Jose, CA, Jan. 17-21, vol. 7526.
  17. J.-S. Lee et al. (2010), “MMSP scalable video database,” [Online]. Available: http://mmspg.epfl.ch/svd.
  18. J.-S. Lee, F. De Simone, and T. Ebrahimi (2011), “Subjective quality eval- uation via paired comparison: Application to scalable video coding,” IEEE Trans. Multimedia, vol. 13, no. 5, pp. 882-893, Oct. 2011
  19. Y. Wang et al. (2008), “Poly@NYU video quality databases,” [Online]. Available: http://vision.poly.edu/index.html/index.php?n=Home- Page.QualityAssessmentDatabase
  20. Y.-F. Ou, T. Liu, Z. Zhao, Z. Ma, and Y. Wang (2008), “Modeling the impact of frame rate on perceptual quality of video,” in Proc. Int. Conf. Image Process. (ICIP), San Diego, CA, Oct. 12-15.
  21. Y.-F. Ou, Z. Ma, and Y. Wang (2009), “A novel quality metric for compressed video considering both frame rate and quantization artifacts,” in Proc. Int. Workshop Video Process. Quality Metrics (VPQM), Scottsdale, AZ, Jan. 15-16.
  22. Y.-F. Ou, Y. Zhou, and Y. Wang (2010), “Perceptual quality of video with frame rate variation: A subjective study,” in Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Dallas, TX, Mar. 14-19, 2010.
  23. X. Feng, T. Liu, D. Yang, and Y. Wang (2008), “Saliency based objective quality assessment of decoded video affected by packet losses,” in Proc. Int. Conf. Image Process. (ICIP), San Diego, CA, Oct. 12–15, 2008.
  24. “VQEG FR-TV Phase I database,” Video Quality Experts Group (VQEG), 2000 [Online]. Available: ftp://ftp.crc.ca/crc/vqeg/TestSe- quences.
  25. “Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment,” VQEG, Apr. 2000 [Online]. Available: http://www.vqeg.org.
  26. “Report on the validation of video quality models for high def- inition video content,” VQEG, Jun. 2010 [Online]. Available: http://www.vqeg.org.
  27. 27.’The Consumer Digital Video Library,” CDVL, 2010 [Online]. Available: http://www.cdvl.org.
  28. R. R. Ramachandra Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake (2019), ‘‘AVT-VQDB-UHD-1: A large scale video quality database for UHD-1,’’ in Proc. IEEE Int. Symp. Multimedia (ISM), Dec. 2019, pp. 1–8. [Online]. Available: https://ieeexplore.ieee.org/document/8959059.
  29. Fan Zhang, Felix Mercer Moss, Roland Baddeley, and David, R. Bull (2018), “BVI-HD: A Video Quality Database for HEVC Compressed and Texture Synthesised Content”, IEEE Trans. on Multimedia.
  30. Mackin, A. and Zhang, F. and Bull, D. (2015), “A study of subjective video quality at various frame rates”, 2015 22nd IEEE International Conference on Image Processing (ICIP).
  31. V. Hosu et al. (2017), «The Konstanz natural video database (KoNViD-1k),» 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1-6, doi: 10.1109/QoMEX.2017.7965673.
  32. P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik (2020), «Subjective and Objective Quality Assessment of High Frame Rate Videos», submitted to IEEE Transactions on Image Processing, [paper].
  33. P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik (2020), «Capturing Video Frame Rate Variations through Entropic Differencing», arXiv preprint arXiv:2006.11424.
  34. X. Yu, N. Birkbeck, Y. Wang, C. G. Bampis, B. Adsumilli and A. C. Bovik, «Predicting the Quality of Compressed Videos with Pre-Existing Distortions«, submitted to IEEE Transactions on Circuits and Systems for Video Technology. [paper].
  35. A. K. Moorthy, L. K. Choi, A. C. Bovik and G. deVeciana (2012), “Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies”, IEEE Journal of Selected Topics in Signal Processing, to appear in October 2012.
  36. A. K. Moorthy, L. K. Choi, G. deVeciana, and A. C. Bovik (2012), “Mobile Video Quality Assessment Database,” IEEE ICC Workshop on Realizing Advanced Video Optimized Wireless Networks, Ottawa, Canada, June 10-15, 2012.
  37. A. K. Moorthy, L. K. Choi, G. deVeciana, and A. C. Bovik (2012), “Subjective Analysis of Video Quality on Mobile Devices,” Sixth International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM) (invited article), Scottsdale, Arizona, January 15-16, 2012.
  38. D. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Jeong, B. Homan and A. C. Bovik, «A Subjective and Objective Study of Space-Time Subsampled Video Quality», submitted to IEEE Transactions on Image Processing. [paper].
  39. C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron, and A. C. Bovik (2017), “Study of Temporal Effects on Subjective Video Quality of Experience,” IEEE Trans. Image Process., vol. 26, no. 11, pp. 5217-5231.
  40. C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron and A. C. Bovik (2016), «LIVE Netflix Video Quality of Experience Database,» Online: http://live.ece.utexas.edu/research/LIVE_NFLXStudy/index.html.
  41. Z. Sinno and A.C. Bovik (2019), «Large-Scale Study of Perceptual Video Quality,” IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 612-627, February 2019.
  42. Z. Sinno and A.C. Bovik (2018), «Large Scale Subjective Video Quality Study,” 2018 IEEE International Conference on Image Processing, Athens, Greece, October 2018.
  43. Z. Sinno and A.C. Bovik (2018), «LIVE Video Quality Challenge Database», Online: http://live.ece.utexas.edu/research/LIVEVQC/ index.html.
  44. J. Y. Lin, R. Song, T. Liu, H. Wang and C.-C. J. Kuo (2015), «MCL-V: A streaming video quality assessment database», J. Vis. Commun. Image Represent., vol. 30, pp. 1-9, Jul. 2015.
  45. “MCL-JCV Dataset”, [online] Available at: http://mcl.usc.edu/mcl- jcv-dataset/.
  46. C. Keimel, J. Habigt, T. Habigt, M. Rothbucher, and K. Diepold (2010), “Visual quality of current coding technologies at high definition IPTV bitrates,” in Multimedia Signal Processing (MMSP), 2010 IEEE International Workshop on, pp. 390-393.
  47. H. Wang et al.(2017), «VideoSet: A large-scale compressed video quality dataset based on JND measurement», J. Vis. Commun. Image Represent., vol. 46, pp. 292-302, Jul. 2017.
  48. Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik (2020), «Patch-VQ: ‘Patching Up’ the Video Quality Problem,» arXiv 2020. [paper]
  49. Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik (2020), «LIVE Large-Scale Social Video Quality (LSVQ) Database», Online:https://github.com/baidut/PatchVQ.
  50. C. G. Bampis, Z.Li, I. Katsavounidis, TY Huang, C. Ekanadham and A. C. Bovik, “Towards Perceptually Optimized End-to-end Adaptive Video Streaming,” submitted to IEEE Transactions on Image Processing.
  51. S. Winkler (2012), “Image and video quality resources,” [Online]. Avail- able: http://stefan.winkler.net/resources.html.
  52. Y. Wang, S. Inguva and B. Adsumilli (2019), «YouTube UGC dataset for video compression research», Proc. IEEE 21st Int. Workshop Multimedia Signal Process. (MMSP), pp. 1-5, Sep. 2019.
  53. P. Mohammadu, A. Ebrahimi-Moghadam, S. Shirani (2015), «Subjective and Objective Quality Assessment of Image: A Survey», Majlesi Journal of Electrical Engineering, vol.9(1), Mar 2015, pp.55-83.
  54. H. Wang et al. (2016), «MCL-JCV: A JND-based H.264/AVC video quality assessment dataset», Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 1509-1513, Sep. 2016
  55. BT-500-11: Methodology for the Subjective Assessment of the Quality of Television Pictures, 2012.
  56. A. Mozhaeva, A. Potashnikov, I. Vlasuyk and L. Streeter (2021), «Constant Subjective Quality Database: The Research and Device of Generating Video Sequences of Constant Quality,» 2021 International Conference on Engineering Management of Communication and Technology (EMCTECH), 2021, pp. 1-5, doi: 10.1109/EMCTECH53459.2021.9618977.