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Proceedings Paper

Robust, high-fidelity coding technique based on entropy-biased ANN codebooks
Author(s): James E. Fowler; Stanley C. Ahalt
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Paper Abstract

We investigate the use of a Differential Vector Quantizer (DVQ) architecture for the coding of digital images. An Artificial Neural Network (ANN) is used to develop entropy-biased codebooks which yield substantial data compression while retaining insensitivity to transmission channel errors. Two methods are presented for variable bit-rate coding using the described DVQ algorithm. In the first method, both the encoder and the decoder have multiple codebooks of different sizes. In the second, variable bit-rates are achieved by encoding using subsets of one fixed codebook. We compare the performance of these approaches under conditions of error-free and error-prone channels.

Paper Details

Date Published: 19 August 1993
PDF: 10 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152619
Show Author Affiliations
James E. Fowler, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

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