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Proceedings Paper

Compressed bit stream classification using VQ and GMM
Author(s): Wenhua Chen; C.-C. Jay Kuo
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Paper Abstract

Algorithms of classifying and segmenting bit streams with different source content (such as speech, text and image, etc.) and different coding methods (such as ADPCM, (mu) -law, tiff, gif and JPEG, etc.) in a communication channel are investigated. In previous work, we focused on the separation of fixed- and variable-length coded bit streams, and the classification of two variable-length coded bit streams by using Fourier analysis and entropy feature. In this work, we consider the classification of multiple (more than two sources) compressed bit streams by using vector quantization (VQ) and Gaussian mixture modeling (GMM). The performance of the VQ and GMM techniques depend on various parameters such as the size of the codebook, the number of mixtures and the test segment length. It is demonstrated with experiments that both VQ and GMM outperform the single entropy feature. It is also shown that GMM generally outperforms VQ.

Paper Details

Date Published: 24 October 1997
PDF: 12 pages
Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); doi: 10.1117/12.284191
Show Author Affiliations
Wenhua Chen, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 3162:
Advanced Signal Processing: Algorithms, Architectures, and Implementations VII
Franklin T. Luk, Editor(s)

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