The Content Based Objective Metrics for Video Quality Evaluation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
The Content Based Objective Metrics for Video Quality Evaluation

Authors: Michal Mardiak, Jaroslav Polec

Abstract:

In this paper we proposed comparison of four content based objective metrics with results of subjective tests from 80 video sequences. We also include two objective metrics VQM and SSIM to our comparison to serve as “reference” objective metrics because their pros and cons have already been published. Each of the video sequence was preprocessed by the region recognition algorithm and then the particular objective video quality metric were calculated i.e. mutual information, angular distance, moment of angle and normalized cross-correlation measure. The Pearson coefficient was calculated to express metrics relationship to accuracy of the model and the Spearman rank order correlation coefficient to represent the metrics relationship to monotonicity. The results show that model with the mutual information as objective metric provides best result and it is suitable for evaluating quality of video sequences.

Keywords: Objective quality metrics, mutual information, region recognition, content based metrics

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081259

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1510

References:


[1] A. B. Watson, J. Hu, and J. F. McGowan, "Dvq: A digital video quality metric based on human vision," Electronic Imaging, vol.10, pp.20-29, 2001.
[2] ITU-R, "Methodology for the subjective assessment of the quality of television pictures," International Telecommunication Union —Radiocommunication Sector, Tech. Rep. BT.500-11, 2002.
[3] J. L. Martinez, P. Cuenca, F. Delicado and F. Quiles, "Objective video quality metrics: A performance analysis," in Proc EUSIPCO Proc., Florence, 2006.
[4] Z. Wang., A. C Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, pp. 1-14, 2004.
[5] F. Xiao, "DCT-based Video Quality Evaluation," MSU Graphics and Media Lab (Video Group), 2000.
[6] K. Seshadrinathan, R. Soundararajan, A. C. Bovik and L. K. Cormack, "Study of Subjective and Objective Quality Assessment of Video", IEEE Trans. on Image Processing, vol.19, pp.1427-1441, June 2010.
[7] K. Seshadrinathan, R. Soundararajan, A. C. Bovik and L. K. Cormack, "A Subjective Study to Evaluate Video Quality Assessment Algorithms", in SPIE Proceedings Human Vision and Electronic Imaging, Jan. 2010.
[8] S. Pechard, D. Barba, and P. Le Callet, "Video quality model based on a spatio-temporal features extraction for H.264-coded HDTV sequences," in Proc. of the Picture Coding Symposium (PCS '07), Lisboa, 2007.
[9] I. Avciba§, B. Sankur and K. Sayood, "Statistical evaluation of image quality measures," J. of Electronic Imaging, vol.11, pp. 206-223, 2002.
[10] D. Androutsos, K. N. Plataniotis and A. N. Venetsanopoulos, "Distance Measures for Color Image Retrieval," in Proc ICIP '98, Chicago, 1998, pp. 770-774.
[11] Z. Cemekova, "Temporal video segmentation and video summarization," Ph.D. dissertation, Comenius Univ., Bratislava, 2009.
[12] C. Li and A. C. Bovik, "Content-weighted video quality assessment using a three-component image model," J. Electronic Imaging, vol. 29, pp. 011003-1-9, 2010.
[13] J. L. Li, G. Chen, and Z. R. Chi, "Image coding quality assessment using fuzzy integrals with a three-component image model," IEEE Trans. Fuzzy Syst., vol. 12, pp. 99-106, 2004.
[14] ANSI T1.801.03, "American National Standard for Telecommunications — Digital transport of one-way video signals — Parameters for objective performance assessment," 2003.
[15] ITU-T, "Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference," Recommendation J.144, 2004.
[16] The Video Quality Experts Group, "Final report from the video quality experts group on the validation of objective quality metrics for video quality assessment, phase II," 2003.