Algorithm based on 2‐bit adaptive delta modulation and fractional linear prediction for Gaussian source coding

Abstract

Abstract A novel 2‐bit adaptive delta modulation (ADM) algorithm is presented based on uniform scalar quantization and fractional linear prediction (FLP) for encoding the signals modelled by a Gaussian probability density function. The study focusses on two major areas: realization of a 2‐bit adaptive quantizer based on Q‐function approximation that significantly facilitates quantizer design; and implementation of a recently introduced FLP approach with the memory of two samples, which replaces the first‐order linear prediction used in standard ADM algorithms and enables improved performance without increasing transmission costs. It furthermore represents the first implementation of FLP in signal encoding, therefore confirming its applicability in a real signal‐processing scenario. Based on the performance analysis conducted on a real speech signal, the proposed ADM algorithm with FLP is demonstrated to outperform other 2‐bit ADM baselines by a large margin for the gain in signal‐to‐noise ratio achieved over a wide dynamic range of input signals. The results of this research indicate that ADM with adaptive quantization based on Q‐function approximation and adaptive FLP represents a promising solution for encoding/compression of correlated time‐varying signals following the Gaussian distribution.

Keywords

• adaptive modulation
• approximation theory
• data compression
• delta modulation
• Gaussian distribution
• quantisation (signal)