Random partial paired comparison for subjective video quality assessment via hodgerank

MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

Volumn , Issue , 2011, Pages 393-402

  Xu, Qianqian ^a   Jiang, Tingting ^b   Yao, Yuan ^c   Huang, Qingming ^a   Yan, Bowei ^b   Lin, Weisi ^d  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b PEKING UNIVERSITY   (China)

^c Peking University   (China)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

HodgeRank; Persistence homology; Random graphs; Subjective video quality assessment

Indexed keywords

BLOCK DESIGNS; CROWDSOURCING; DATA COLLECTION; DESIGN PRINCIPLES; HODGE DECOMPOSITION; HODGERANK; IMBALANCED DATA; IN-BUILDINGS; PAIRED COMPARISON; PERSISTENCE HOMOLOGY; RANDOM GRAPH THEORY; RANDOM GRAPHS; RANDOM SAMPLE; RATING PROCEDURES; SUBJECTIVE VIDEO QUALITY; VISUAL QUALITY EVALUATION; VISUAL QUALITY METRICS;

DESIGN; DESIGN OF EXPERIMENTS; GRAPH THEORY; IMAGE QUALITY; INTERNET; RATING; VIDEO SIGNAL PROCESSING;

QUALITY CONTROL;

EID: 84455161705     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2072298.2072350     Document Type: Conference Paper

References (35)

1. ITU-R Recommendation P.800. Methods for subjective determination of transmission quality, 1996.
2. ITU-R Methodology for the Subjective Assessment of the Quality of Television Pictures. 2002.
3. LIVE video quality assessment database. http://live.ece.utexas.edu/ research/quality/, 2008.
4. O. Alonso, D. Rose, and B. Stewart. Crowdsourcing for relevance evaluation. SIGIR Forum, 42(2):9-15, 2008.
5. K. Arrow. A difficulty in the concept of social welfare. Journal of Political Economy, 58(4):328-346, 1950.
6. D. Brabham. Crowdsourcing as a model for problem solving: an introduction and cases. Convergence, 14(1):75, 2008.
7. G. Carlsson. Topology and data. Bulletin of the American Mathematical Society, 46(2):255-308, 2009.
8. K.-T. Chen, C.-C. Wu, Y.-C. Chang, and C.-L. Lei. A crowdsourceable QoE evaluation framework for multimedia content. pages 491-500. ACM Multimedia, 2009.
9. H. David. The method of paired comparisons. 2nd Ed., Griffin's Statistical Monographs and Courses, 41. Oxford University Press, New York, NY, 1988.
10. H. Edelsbrunner and J. Harer. Computational topology : an introduction. 2010.
11. H. Edelsbrunner, D. Letscher, and A. Zomorodian. Topological persistence and simplification. Discrete and Computational Geometry, 28(4):511-533, 2002.
12. P. Erdos and A. Renyi. On random graphs i. Publ. Math. Debrecen, 6:290-297, 1959.
13. Y. Freund, R. Iyer, R. Shapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4(6):933-969, 2004.
14. T. Hastie and R. Tibshirani. Classification by pairwise coupling. Annals of Statistics, 26(2):451-471, 1998.
15. A. Hekstra, J. Beerends, D. Ledermann, F. de Caluwe, S. Kohler, R. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss. PVQM-A perceptual video quality measure. Signal Processing: Image Communication, 17:781-798, 2002.
16. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. MIT Press, 2000.
17. J. Howe. The rise of crowdsourcing. Wired Magazine, 14(6):176-183, 2006.
18. X. Jiang, L.-H. Lim, Y. Yao, and Y. Ye. Statistical ranking and combinatorial Hodge theory. Mathematical Programming, 2010.
19. M. Kahle. Topology of random clique complexes. Discrete Mathematics, 309:1658-1671, 2009.
20. Kendall, Maurice, and J. Gibbons. Rank Correlation Methods. Oxford University Press, 1990.
21. M. Kendall and B. Smith. On the method of paired comparisons. Biometrika, 31(3-4):324-345, 1940.
22. A. Kittur, E. Chi, and B. Suh. Crowdsourcing user studies with Mechanical Turk. pages 453-456. SIGCHI conference on Human factors in computing systems, 2008.
23. I. Koutis, G. Miller, and R. Peng. Approaching optimality for solving sdd systems. FOCS'10 51st Annual IEEE Symposium on Foundations of Computer Science, 2010.
24. J. Lubin. The use of psychophysical data and models in the analysis of display system performance. pages 163-178. Digital Images and Human Vision, 1993.
25. G. Noether. Remarks about a paired comparison model. Psychometrika, 25:357-367, 1960.
26. S. Nowak and S. Ruger. How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. International conference on Multimedia information retrieval, 2010.
27. M. Pinson and S.Wolf. A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3):312-322, September 2004.
28. T. Saaty. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3):234-281, 1977.
29. H. Sexton and M. Johansson. JPlex: a java software package for computing the persistent homology of filtered simplicial complexes. http://comptop. stanford.edu/programs/jplex/, 2009.
30. A. Sorokin and D. Forsyth. Utility data annotation with Amazon Mechanical Turk. pages 1-8. Computer Vision and Pattern Recognition Workshops, June 2008.
31. D. Spielman and S.-H. Teng. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. STOC'04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, 2004.
32. L. Thurstone. The method of paired comparisons for social values. Journal of Abnormal and Social Psychology, 27:384-400, 1927.
33. C. van den Branden Lambrecht and O. Verscheure. Perceptual quality measure using a spatiotemporal model of the human visual system. SPIE Conference on Human Vision and Electronic Imaging, 2668(1):450-461, March 1996.
34. Z. Wang, L. Lu, and A. Bovik. Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication, 19(2):121-132, February 2004.
35. A. Zomorodian and G. Carlsson. Computing persistent homology. Discrete and Computational Geometry, 33(2):249-274, 2005.