A Dual-Mode 2-bit Uniform Scalar Quantizer for Data with Laplacian Distribution

In this paper, a novel dual-mode 2-bit scalar quantizer based on uniform quantization of data with Laplacian distribution is proposed. It is composed of two uniform quantizers with different step size, whereby the rule for quantizer selection is determined in order to provide the highest signal to quantization noise ratio in the given range of data variance. The proposed dual-mode scalar quantizer also performs frame-by-frame processing. It is shown that in terms of signal to quantization noise ratio our proposal is able to outperform single uniform quantizer in a wide dynamic range of input data variances. To verify benefits achieved with the proposed dual-mode quantizer the experiment is performed with the Multi-Layer Perceptron (MLP) weights.