Abstract
At present, video contents from the Internet are accessed with increasing frequency. In this paper, combined with China Mobile Multimedia Broadcasting (CMMB) system characteristics is presented based on high-dimension space computation modal for measurement of quantity of CMMB video sequences. From the relationship between different points, it makes computation in high-dimension space for the measurement of videos. Different with some classic algorithms, such as PSNR, objective model, which discussed for alignment of video sequences and lead to complex computation, the proposed method is based on computation in high-dimension space. Image sequences of original video are classified into different sets. For real quality measurement, a CMMB image is used to find similar among these sets and gave its measurement. Experimental results indicate that the proposed method make the measurement easily and meet the real noised image sequence. The proposed method is constructive and it proves the reliability of this measurement system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
China Multimedia Mobile Broadcasting (EB/OL) (December 04, 2010), http://en.wikipedia.org/wiki/CMMB
Myasnikov, V.V., Ivanov, A.A., Gashnikov, M.V., Myasnikov, E.V.: Computer program for automatic estimation of digital image quality 21(3), 415–418 (2011)
ITU-T Recommendation BT.1788, Methodology for the subjective assessment of video quality in multimedia applications (2007)
Soundararajan, R.: RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment. IEEE Transactions on Image Processing 21(2), 517–526 (2012)
Zhang, L., Mou, X., Zhang, D.: FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011)
Ninassi, A., Meur, O.L., Callet, P.L., Barbba, D.: Does where you gaze on an image affect your perception of quality? Applying visual attention to image quality metric. In: Proc. IEEE Int. Conf. Image Process, ICIP 2007, vol. 2, pp. 169–172 (2007)
Shoujue, W., Jiangliang, L.: Geometrical Learning, descriptive geometry, and biometric pattern recognition. Neuron Computing 67, 9–28 (2005)
Wang, S.J.: Bionic(topological)pattern recognition-A new model of pattern recognition theory and its applications, Acta Electron. Sinica 30(10), 1–4 (2002)
Zhu, S., Wang, Z., Liao, M.: Research on K-classification Covering for PONN. Computer Application 27(2), 330–332 (2007) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, J., Zhu, Sj., Bi, Zq. (2012). CMMB Image Sequences Measurement Based on Computation in High-Dimension Space. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-33478-8_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
eBook Packages: Computer ScienceComputer Science (R0)