Joint Source Channel Coding Using Arithmetic Codes

Joint Source Channel Coding Using Arithmetic Codes

Bi Dongsheng, Khalid Sayood, Michael Hoffman
ISBN: 9781608451487 | PDF ISBN: 9781608451494
Copyright © 2009 | 77 Pages | Publication Date: 01/01/2009

BEFORE YOU ORDER: You may have Academic or Corporate access to this title. Click here to find out: 10.2200/S00222ED1V01Y200911COM004

Ordering Options: Paperback $30.00   E-book $24.00   Paperback & E-book Combo $37.50

Why pay full price? Members receive 15% off all orders.
Learn More Here

Read Our Digital Content License Agreement (pop-up)

Purchasing Options:


Based on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. In this monograph, we propose a new way of looking at arithmetic codes with forbidden symbols. If a limit is imposed on the maximum value of a key parameter in the encoder, this modified arithmetic encoder can also be modeled as a finite state machine and the code generated can be treated as a variable-length trellis code. The number of states used can be reduced and techniques used for decoding convolutional codes, such as the list Viterbi decoding algorithm, can be applied directly on the trellis. The finite state machine interpretation can be easily migrated to Markov source case. We can encode Markov sources without considering the conditional probabilities, while using the list Viterbi decoding algorithm which utilizes the conditional probabilities. We can also use context-based arithmetic coding to exploit the conditional probabilities of the Markov source and apply a finite state machine interpretation to this problem.

The finite state machine interpretation also allows us to more systematically understand arithmetic codes with forbidden symbols. It allows us to find the partial distance spectrum of arithmetic codes with forbidden symbols. We also propose arithmetic codes with memories which use high memory but low implementation precision arithmetic codes. The low implementation precision results in a state machine with less complexity. The introduced input memories allow us to switch the probability functions used for arithmetic coding. Combining these two methods give us a huge parameter space of the arithmetic codes with forbidden symbols. Hence we can choose codes with better distance properties while maintaining the encoding efficiency and decoding complexity. A construction and search method is proposed and simulation results show that we can achieve a similar performance as turbo codes when we apply this approach to rate 2/3 arithmetic codes.

Table of Contents: Introduction / Arithmetic Codes / Arithmetic Codes with Forbidden Symbols / Distance Property and Code Construction / Conclusion

Table of Contents

Introduction
Arithmetic Codes
Arithmetic Codes with Forbidden Symbols
Distance Property and Code Construction
Conclusion

About the Author(s)

Bi Dongsheng, University of Nebraska

Khalid Sayood, University of Nebraska

Michael Hoffman, University of Nebraska

Reviews
Browse by Subject
ACM Books
IOP Concise Physics
0 items
LATEST NEWS

Newsletter
Note: Registered customers go to: Your Account to subscribe.

E-Mail Address:

Your Name: