Share Email Print

Proceedings Paper

Bit-stream classification using joint and conditional entropies
Author(s): Wenhua Chen; C.-C. Jay Kuo
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 30 September 1996
PDF: 12 pages
Proc. SPIE 2898, Electronic Imaging and Multimedia Systems, (30 September 1996); doi: 10.1117/12.253380
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. 2898:
Electronic Imaging and Multimedia Systems
Chung-Sheng Li; Robert L. Stevenson; LiWei Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?