Quality Assessment for MPEG-2 Video Streams

  • Caihong Wang
  • Xiuhua Jiang
  • Yuxia Wang
  • Fang Meng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 157)


Depending on different contents of video sequences, the compression settings also differ. So many parameters can be obtained from video steams. We extract and analyze a large set of parameters from different layer. Based on the correlation between features and the subjective perceived quality, the most important objective parameters are picked out. A low complexity objective quality assessment metric is obtained by a linear calculation on the selected parameters. The presented method can perform continuous objective quality assessment in unit of GOP (Group of Pictures). The experimental results show that our model can achieve good performance for video quality prediction. In addition, our model does not require the source, or the decoded picture, it is suitable for real-time applications. And continuous quality assessment can provide an automatic warning without delay when picture quality problems occur.


Video Quality Mean Opinion Score Image Quality Assessment Compression Setting Video Quality Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Winkler, S., Mohandas, P.: The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics. IEEE Trans. on Broadcasting 54, 660–668 (2008)CrossRefGoogle Scholar
  2. 2.
    Lubin, J.: The use of psychophysical data and models in the analysis of display system performance. In: Waston, A.B. (ed.) Digital Images and Human Vision, pp. 163–178. MIT press (1993)Google Scholar
  3. 3.
    Pinson, M.H., Wolf, S.: A New Standardized Method for Objectively Measuring Video Quality. IEEE Trans. on Broadcasting, 312–322 (2004)Google Scholar
  4. 4.
    Wang, Z., Lu, L., Bovik, A.C.: Video Quality Assessment Based on Structural Distortion Measurement. Signal Processing: Image Communication 19(2), 121–132 (2004)CrossRefGoogle Scholar
  5. 5.
    Wang, X., Tian, B., Liang, C., et al.: Blind Image Quality Assessment for Measuring Image Blur. IEEE Congress on Image and Signal Processing, 467–470 (2008)Google Scholar
  6. 6.
    Farias, M.C.Q., Mitra, S.K.: No-reference video quality metric based on artifact measurements. In: IEEE International Conference on Image Processing, September 2005, vol. 3, pp. 141–144 (2005)Google Scholar
  7. 7.
    Koumaras, H., Kourtis, A., Martakos, D.: Evaluation of Video Quality Based on Objectively Estimated Metric (2005)Google Scholar
  8. 8.
    Ries, M., Nemethova, O., Rupp, M.: Motion Based Reference-Free Quality Estimation for H.264/AVC Video Streaming. In: International Symposium on Wireless Pervasive Computing (February 2007)Google Scholar
  9. 9.
    Gastaldo, P., Rovetta, S., Zunino, R.: Objective quality assessment of MPEG-2 video streams by using CBP neural networks. IEEE Transactions on Neural Networks, 939–947 (July 2002)Google Scholar
  10. 10.
    Jiang, X., Wang, X., Wang, C.: No-reference video quality assessment for MPEG-2 video streams using BP neural networks. In: International Conference on Interaction Sciences: Information Technology, Culture and Human (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Caihong Wang
    • 1
  • Xiuhua Jiang
    • 1
  • Yuxia Wang
    • 1
  • Fang Meng
    • 1
  1. 1.School of Information EngineeringCommunication University of ChinaBeijingChina

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