Robust, high-fidelity coding technique based on entropy-biased ANN codebooks

James E. Fowler; Stanley C. Ahalt

doi:10.1117/12.152619

19 August 1993 Robust, high-fidelity coding technique based on entropy-biased ANN codebooks

James E. Fowler, Stanley C. Ahalt

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152619
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

James E. Fowler and Stanley C. Ahalt "Robust, high-fidelity coding technique based on entropy-biased ANN codebooks", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152619

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