Robust, variable bit-rate coding using entropy-biased codebooks

Abstract

The authors demonstrate the use of a differential vector quantization (DVQ) architecture for the coding of digital images. An artificial neural network is used to develop entropy-biased codebooks which yield substantial data compression without entropy coding and are very robust with respect 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 using subsets of one fixed codebook. The performance of these approaches is compared, under conditions of error-free and error-prone channels. Results show that this coding technique yields pictures of excellent visual quality at moderate compression rate.<>