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

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.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Performance Analysis of a 2-bit Dual-Mode Uniform Scalar Quantizer for Laplacian Source

“…An extensive amount of research is dedicated to this topic, where different aspects were considered during the analysis and valuable conclusions were derived [9, 15−17]. That indeed helped to promote the USQ as an excellent candidate for various practical resource-constrained applications, such as speech coding [18] or neural network (NN) compression [1][2][3]23].…”

“…Specifically, we investigate the variance-mismatch effect in case of 2-bit USQ and propose an adequate measure to deal with it. Note also that the practical significance of 2-bit quantizers is large, as they have already been successively applied in several data processing applications such as speech coding [18,19], image coding [11,29] or, as mentioned, NN compression [4,5,20,21,26]. Regarding the implementation in NN compression, it was shown in [4,5,26] that 2-bit USQ can enable a good trade-off between performance and size of NN.…”

“…Note that such solutions may be adequate for NN compression, since NNs are more robust to change in data quality than e.g. speech, where adaptive solutions are more preferable, as shown in [18,19]. A 2-bit quantizer model for NN weights, which is non-adaptive and non-uniform one (based on logarithmic compression function) with increased robustness property was proposed in [20].…”