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

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-based 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.