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Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home

by Marcio L. Graciano, Alexandre R. S. Romariz, Jose Camargo Da Costa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 16
Year of Publication: 2013
Authors: Marcio L. Graciano, Alexandre R. S. Romariz, Jose Camargo Da Costa
10.5120/10554-5750

Marcio L. Graciano, Alexandre R. S. Romariz, Jose Camargo Da Costa . Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home. International Journal of Computer Applications. 63, 16 ( February 2013), 37-42. DOI=10.5120/10554-5750

@article{ 10.5120/10554-5750,
author = { Marcio L. Graciano, Alexandre R. S. Romariz, Jose Camargo Da Costa },
title = { Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 16 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 37-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number16/10554-5750/ },
doi = { 10.5120/10554-5750 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:33.022137+05:30
%A Marcio L. Graciano
%A Alexandre R. S. Romariz
%A Jose Camargo Da Costa
%T Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 16
%P 37-42
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this work, a methodology for objective evaluation of the quality of video programs, without reference, recording these programs in the users' residence using a video camera is presented. Themethodology is based on the use of a digital watermark embedded in the original program. The watermark is invisible to the user, but capturable by the video camera. The recorded video is handled by specific software that evaluates the watermark degradation. The measure of degradation of this watermark is used to estimate the quality of the video broadcasting system. A case study is presented to validate the methodology. The results of video quality metrics using this methodology were compared to a standardized full reference metrics and the linear correlation between these metrics was superior to 93%, which indicates a high convergence. The result of video quality metrics were also compared to a pixel based difference metrics, PSNR (Peak Signal to Noise Ratio) and the linear correlation was superior to 99%.

References
  1. S. Daly. The visible differences predictor: an algorithm for the assessment of image fidelity. In Andrew B. Watson, editor, Digital Images and Human Vision, pages 179–206, Cambridge, Massachusetts, 1993. MIT Press.
  2. J. B. DeVelis and G. B. Parrent. Transfer function for cascaded optical systems. J. Opt. Soc. Am, 57:1486–1490, 1967.
  3. M. Estribeau and P. Magnan. Fast mtf measurement of cmos imagers using iso 12233 slanted edge methodology. In SPIE Detectors and Associated Signal Processing, volume 5251, pages 243–251, 2004.
  4. M. C. Q. Farias, M. Carli and S. K. Mitra. Objective vídeo quality metric based on data hiding. IEEE Transactions on Consumer Electronics, 51:983–992, 2005.
  5. M. C. Q. Farias and S. K. Mitra. No-reference video quality metric based on artifact measurements. In IEEE International Conference on Image Processing, volume 3, pages 141–144, 2005.
  6. M. C. Q. Farias and S. K. Mitra. A methodology for designing no-reference video quality metrics. In Fourth International Workshop on Video Processing and Quality Metrics for Consumer Electronics, pages 1–6, 2009.
  7. J. W. Goodman. Introduction to Fourier Optics. McGraw-Hill Physical and Quantum Electronics Series, 1968.
  8. M. L. Graciano, A. R. S. Romariz and J. C. Costa. Cmos image sensor device for objective evaluation of video quality in mass distribution networks. In IEEE 7th Consumer Communications and Networking Conference (CCNC), pages 1–2, 2010.
  9. E. M. Grainger and K. N. Cupery. An optical merit function (sqf) which correlates with subjective image judgments. Photographic Science and Engineering, 16:221–230, 1972.
  10. ITU-R. Recommendation BT. 500-13, chapter Methodology for the subjective assessment of the quality of television pictures. Recommendations of the ITU, Radiocommunication Sector, 2012.
  11. ITU-T. Final report from the video quality experts group (VQEG) on the validation of objective models of video quality assessment, volume 4, chapter COM 9-80-E. approved for release at VQEG meeting, 2000.
  12. B. W. Keelan. Objective and subjective measurement and modeling of image quality: a case study. In SPIE Applications of Digital Image Processing XXXIII, volume 7798, pages 779–815, 2010.
  13. L. Li, B. Guo and L. Guo. Rotation, scaling and translation invariant image watermarking using feature points. The Journal of China Universities of Posts and Telecommunications, 15:82–87, 2008.
  14. W. Lin and C. C. Jay Kuo. Perceptual visual quality metrics: A survey. Journal of Visual Communication and Image Representation, 22(4):297–312, 2011.
  15. H. Loukil, M. H. Kacem and M. S. BouhleL. A new image quality metric using system visual human characteristics. International Journal of Computer Applications, 60(6):32–36, 2012.
  16. A. K. Moorthy and A. C. Bovik. Visual quality assessment algorithms : What does the future hold? International Journal of Multimedia Tools and Applications, Special Issue on Survey Papers in Multimedia by World Experts, 51(2):675–696, 2011.
  17. M. Pinson and S. Wolf. Video Quality Measurement Users Manual. NTIA Handbook HB-02-01, 2002.
  18. M. H. Pinson and S. Wolf. A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50:312–322, 2004.
  19. S. Poongodi and B. Kalaavathi. Comparative study of various transformations in robust watermarking algorithms. International Journal of Computer Applications, 58(11), 2012.
  20. F. De Simone, L. Goldmann, J. S. Lee and T. Ebrahimi. Towards high efficiency video coding: Subjective evaluation of potential coding technologies. Journal of Visual Communication and Image Representation, 22(8):734–748, 2011.
  21. VQEG. Final report from the video quality experts group on the validation of objective models of video quality assessment - Phase II. Tech. Report, 2003.
  22. X. Wang, C. Wang, H. Yang, and P. Niu. A robust blind color image watermarking in quaternion fourier transform domain. Journal of Systems and Software, 86(2):255–277, 2013.
  23. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13:600–612, 2004.
  24. S. Winkler and P. Mohandas. The evolution of video quality measurement: From psnr to hybrid metrics. IEEE Transactions on Broadcasting, 54:660–668, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

video quality quality metrics human visual system modulation transfer function

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