Switched Quasi-Logarithmic Quantizer with Golomb–Rice Coding

This paper proposes a model of switched quasilogarithmic quantizer for speech signal based on G.711 standard with usage of Golomb-Rice (GR) coding. In order to achieve better performances a method with switched quantizer is applied. Variance range is split into quantizers and for each of them a separate quantizer is designed, i.e. the support region is determined. Optimization of the support region and choice of the parameter μ is done in order to obtain a quantizer that obeys G.712 standard and gives minimal average bit rate. Every quantizer within the variance range has own model with a two-stage coder. Two stages are introduced with purpose to reduce the bit rate, whereby GR code plays its role as Variable Length Code (VLC). The first stage uses a GR coder for coding segments of the quantizer's support region, whereas the second stage applies the coding method with fixed code lengths for coding cells within a segment. GR has simpler and cheaper hardware realization than other VLC codes, Huffman's for instance, with very satisfying results regarding quality of quantized signal

SWITCHED UNIFORM SCALAR QUANTIZATION ADAPTED TO MEAN AND VARIANCE FOR SPEECH CODING

Average power and variance are widely used in adaptation techniques in signal coding. A speech signal is usually assumed to be zero-mean; thus an average signal power is equal to the signal variance. However, this assumption is valid only for longer signals with a large number of samples. When the signal is divided into frames (especially if the number of samples within the frame is small) the speech signal within the frame may not be zero-mean. Hence, frame-by-frame adaptation to signal mean might be beneficial. A switched uniform scalar quantizer with adaptation to signal mean and variance is proposed in this paper. The analysis is performed for different frame lengths and the results are compared to an adaptive uniform quantizer that uses adaptation only to average signal power, showing an improved performance. Signal to quantization noise ratio (SQNR) is used as a performance measure

Projektovanje kvantizera za primenu u obradi signala i neuronskim mrežama

Scalar quantizers are present in many advanced systems for signal processing and transmission, аnd their contribution is particular in the realization of the most important step in digitizing signals: the amplitude discretization. Accordingly, there are justified reasons for the development of innovative solutions, that is, quantizer models which offer reduced complexity, shorter processing time along with performance close to the standard quantizer models. Designing of a quantizer for a certain type of signal is a specific process and several new methods are proposed in the dissertation, which are computationally less intensive compared to the existing ones. Specifically, the design of different types of quantizers with low and high number of levels which apply variable and a fixed length coding, is considered. The dissertation is organized in such a way that it deals with the development of coding solutions for standard telecommunication signals (e.g. speech), as well as other types of signals such as neural network parameters. Many solutions, which belong to the class of waveform encoders, are proposed for speech coding. The developed solutions are characterized by low complexity and are obtained as a result of the implementation of new quantizer models in non-predictive and predictive coding techniques. The target of the proposed solutions is to enhance the performance of some standardized solutions or some advanced solutions with the same/similar complexity. Testing is performed using the speech examples extracted from the well-known databases, while performance evaluation of the proposed coding solutions is done by using the standard objective measures. In order to verify the correctness of the provided solutions, the matching between theoretical and experimental results is examined. In addition to speech coding, in dissertation are proposed some novel solutions based on the scalar quantizers for neural network compression. This is an active research area, whereby the role of quantization in this area is somewhat different than in the speech coding, and consists of providing a compromise between performance and accuracy of the neural network. Dissertation strictly deals with the low-levels (low-resolution) quantizers intended for post-training quantization, since they are more significant regarding compression. The goal is to improve the performance of the quantized neural network by using the novel designing methods for quantizers. The proposed quantizers are applied to several neural network models used for image classification (some benchmark dataset are used), and as performance measure the prediction accuracy along with SQNR is used. In fact, there was an effort to determine the connection between these two measures, which has not been investigated sufficiently so far