Bit-stream classification using joint and conditional entropies

Wenhua Chen; C.-C. Jay Kuo

doi:10.1117/12.253380

30 September 1996 Bit-stream classification using joint and conditional entropies

Wenhua Chen, C.-C. Jay Kuo

Proceedings Volume 2898, Electronic Imaging and Multimedia Systems; (1996) https://doi.org/10.1117/12.253380
Event: Photonics China '96, 1996, Beijing, China

Abstract

We are interested in the problem of classifying and segmenting bit streams with different source content and with different source coding in a communication channel. Although there are many researches on data segmentation, not much work is seen on this particular problem. Given zero and one observations of the bit stream, we first show that the windowed discrete FOurier transform enables us to distinguish fixed and variable length coded bit streams and in the case of fixed lengths coded bit stream, it can also determine the coding length of the bit stream. To further separate bit streams with variable length codes, we propose a classifier based on k-bit joint and conditional entropies. We present the joint and conditional entropy estimation schemes, and provide the upper bound for their performance. Then, we analyze the computational complexity of the entropy estimation. Finally experimental results are given to demonstrate the discriminant power of proposed entropy features.

Citation Download Citation

Wenhua Chen and C.-C. Jay Kuo "Bit-stream classification using joint and conditional entropies", Proc. SPIE 2898, Electronic Imaging and Multimedia Systems, (30 September 1996); https://doi.org/10.1117/12.253380

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