A Machine Learning Approach to No-Reference Objective Video Quality Assessment for High Definition Resources
الموضوعات : journal of Artificial Intelligence in Electrical Engineeringناصر فرج زاده 1 , ملیحه مظلومی 2
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الکلمات المفتاحية: Video quality assessments, Generalized Gaussian distribution, Wavelet Transform,
ملخص المقالة :
The video quality assessment must be adapted to the human visual system, which is why researchers have performed subjective viewing experiments in order to obtain the conditions of encoding of video systems to provide the best quality to the user. The objective of this study is to assess the video quality using image features extraction without using reference video. RMSE values and processing time of SVR for BMP and JPEG formats in quality assessment were 0.78×10-2, 0.81×10-2, 6.0s and 4.8s, respectively. In this study, a metric system for no-reference assessing the video quality is presented using wavelet transform and generalized Gaussian distribution parameters. Results of ITU-BT tests for each video were used to train SVR and its performance for video frames is evaluated
1] Juan Pedro López Velasco (2012). Video Quality Assessment, Video Compression, Dr. Amal Punchihewa (Ed.), ISBN: 978-953-51- 0422-3, InTech.
[2] M. P. Eckert and A. P. Bradley, "Perceptual quality metrics applied to still image compression," Signal Processing, vol. 70, pp. 177-200, Nov. 1998
[3] A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance,” IEEE Trans Communications, vol. 43, pp. 2959- 2965, Dec. 1995.
[4] ITU-R Recommendation BT.500-11, “Methodology for the subjective assessment of the quality of television pictures,” Geneva, 2002 (available at www.itu.org).
[5] Y. K. Lai and C.-C. J. Kuo, "A Haar wavelet approach to compressed image quality measurement," Journal of Visual Communication and Image Understanding, vol. 11, pp. 17-40, Mar. 2000.
[6] Z. Wang, A. C. Bovik and L. Lu, “Why is image quality assessment so difficult?” Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Proc., vol. 4, pp. 3313-3316, May 2002.
[7] J. Lubin, “A visual discrimination model for image system design and evaluation,” in Visual Models for Target Detection and Recognition, E. Peli, ed., pp. 207-220, Singapore: World Scientific Publisher, 1995.
[8] Zhou Wang, Hamid R. Sheikh and Alan C. Bovik, OBJECTIVE VIDEO QUALITY ASSESSMENT, Chapter 41 in The Handbook of Video Databases: Design and Applications, B. Furht and O. Marqure, ed., CRC Press, pp. 1041-1078, September 2003.
[9] Z. Wang, A. C. Bovik and B. L. Evans, “Blind measurement of blocking artifacts in images,” Proc. IEEE Int. Conf. Image Proc., vol. 3, pp. 981-984, Sept. 2000.
[10] P. Gastaldo, S. Rovetta and R. Zunino, “Objective assessment of MPEG-video quality: a neural-network approach,” in Proc. IJCNN, vol. 2, pp. 1432-1437, 2001.
[11] Kawayokeita, Y. ; Horita, Y., NR objective continuous video quality assessment model based on frame quality measure, Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on, Year: 2008, Page(s): 385 – 388.
[12] N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, "Image quality assessment based on a degradation model," IEEE Trans. Image Processing, vol. 4, pp. 636- 650, Apr. 2000.
[13] Sharifi, K. and Leon-Garcia, A. (1995). Estimation of shape parameter for generalized Gaussian distribution in subband decomposition of video. IEEE Trans. onCircuits and Systems for Video Technology, Vol 5, No. 1 Feb. 1995 pp. 52-56
[14] Choi, S. Cichocki, A. and Amari, S. (2000) Local stability analysis of flexible independent component analysis algorithm. Proceedings of 2000 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2000, Istanbul, Turkey, June 5-9, 2000, pp. 3426-3429.
[15] Wu, H.-C. y Principe, J. (1998). Minimum entropy algorithm for source separation. Proceedings of the 1998 Mindwest symposium on Systems and Circuits.
[16] J. M. Shapiro, “Embedded image coding using zerotrees of wavelet co-efficient,” vol. 41, pp. 3445-3462, Dec. 1993.
[17] Drucker, Harris; Burges, Christopher J. C.; Kaufman, Linda; Smola, Alexander J.; and Vapnik, Vladimir N. (1997); "Support Vector Regression Machines", in Advances in Neural Information Processing Systems 9, NIPS 1996, 155–161, MIT Press.
[18] R. C. Gonzalez and R. E. Woods, Digital Image Processing (2nd Edition). Prentice Hall, 2002.
