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Proceedings Paper

Statistics of natural image sequences: temporal motion smoothness by local phase correlations
Author(s): Zhou Wang; Qiang Li
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Paper Abstract

Statistical modeling of natural image sequences is of fundamental importance to both the understanding of biological visual systems and the development of Bayesian approaches for solving a wide variety of machine vision and image processing problems. Previous methods are based on measuring spatiotemporal power spectra and by optimizing the best linear filters to achieve independent or sparse representations of the time-varying image signals. Here we propose a different approach, in which we investigate the temporal variations of local phase structures in the complex wavelet transform domain. We observe that natural image sequences exhibit strong prior of temporal motion smoothness, by which local phases of wavelet coefficients can be well predicted from their temporal neighbors. We study how such a statistical regularity is interfered with "unnatural" image distortions and demonstrate the potentials of using temporal motion smoothness measures for reduced-reference video quality assessment.

Paper Details

Date Published: 10 February 2009
PDF: 12 pages
Proc. SPIE 7240, Human Vision and Electronic Imaging XIV, 72400W (10 February 2009); doi: 10.1117/12.810176
Show Author Affiliations
Zhou Wang, Univ. of Waterloo (Canada)
Qiang Li, The Univ. of Texas at Arlington (United States)

Published in SPIE Proceedings Vol. 7240:
Human Vision and Electronic Imaging XIV
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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