Compressed bit stream classification using VQ and GMM

Wenhua Chen; C.-C. Jay Kuo

doi:10.1117/12.284191

24 October 1997 Compressed bit stream classification using VQ and GMM

Wenhua Chen, C.-C. Jay Kuo

Proceedings Volume 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII; (1997) https://doi.org/10.1117/12.284191
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

Wenhua Chen and C.-C. Jay Kuo "Compressed bit stream classification using VQ and GMM", Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); https://doi.org/10.1117/12.284191

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