Frog-in-the-box index codes with maximum likelihood decoding for robust scalar quantization

Abstract

The performance of frog-in-the box (FIB) code index assignments is numerically investigated when maximum likelihood (ML) decoding is used in place of optimal MMSE decoding. Specifically, the reproduction levels are chosen to be those that would be used for a noiseless channel, and the decoder simply maps the channel output to a level whose index codeword is closest to the channel output.