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Proceedings Paper

Video compression with wavelets and random neural network approximations
Author(s): Fan Hai; Khaled F. Hussain; Erol Gelenbe; Ratan Kumar Guha
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Paper Abstract

Modern video encoding techniques generate variable bit rates, because they take advantage of different rates of motion in scenes, in addition to using lossy compression within individual frames. We have introduced a novel method for video compression based on temporal subsampling of video frames, and for video frame reconstruction using neural network based function approximations. In this paper we describe another method using wavelets for still image compression of frames, and function approximations for the reconstruction of subsampled frames. We evaluated the performance of the method in terms of observed traffic characteristics for the resulting compressed and subsampled frames, and in terms of quality versus compression ratio curves with real video image sequences. Comparisons are presented with other standard methods.

Paper Details

Date Published: 4 April 2001
PDF: 8 pages
Proc. SPIE 4305, Applications of Artificial Neural Networks in Image Processing VI, (4 April 2001); doi: 10.1117/12.420926
Show Author Affiliations
Fan Hai, Univ. of Central Florida (United States)
Khaled F. Hussain, Univ. of Central Florida (United States)
Erol Gelenbe, Univ. of Central Florida (United States)
Ratan Kumar Guha, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 4305:
Applications of Artificial Neural Networks in Image Processing VI
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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