Zoran H. Peric

Design of signal constellations for Gaussian channel by using iterative polar quantization

The new iterative nonuniform polar quantization method is presented in this paper. The decision levels and the reconstruction levels are determined by this iterative method as well as the number of points on levels. The quantization mean-squared error (MSE) is used as the criterion for optimization. We also present the exact method for the error probability determination per signal constellation symbol, which is obtained by this quantization method. The error probability for nonequiprobable symbols transmission through a channel with the additive Gaussian noise is also computed.

Low Complex Forward Adaptive Loss Compression Algorithm and Its Application in Speech Coding

Low Complex Forward Adaptive Loss Compression Algorithm and Its Application in Speech Coding This paper proposes a low complex forward adaptive loss compression algorithm that works on the frame by frame basis. Particularly, the algorithm we propose performs frame by frame analysis of the input speech signal, estimates and quantizes the gain within the frames in order to enable the quantization by the forward adaptive piecewise linear optimal compandor. In comparison to the solution designed according to the G.711 standard, our algorithm provides not only higher level of the average signal to quantization noise ratio, but also performs a reduction of the PCM bit rate for about 1 bits/sample. Moreover, the algorithm we propose completely satisfies the G.712 standard, since it provides overreaching the curve defined by the G.712 standard in the whole of variance range. Accordingly, we can reasonably believe that our algorithm will find its practical implementation in the high quality coding of signals, represented with less than 8 bits/sample, which as well as speech signals follow Laplacian distribution and have the time varying variances.

Multidimensional Optical Transport Based on Optimized Vector-Quantization-Inspired Signal Constellation Design

An optimized vector-quantization-inspired signal constellation design (OVQ-SCD) suitable for multidimensional optical transport is proposed, in which signal constellation radii transformation function is optimized and near-uniform distribution of points is achieved. The proposed OVQ-SCD is used in a tandem with a hybrid multidimensional coded-modulation scheme employing Slepian sequences as electrical discrete-time basis functions, orthogonal prolate spheroidal wave functions as impulse responses of optical filters in orthogonal-division multiplexing, and spatial modes as optical continuous-time basis functions. It has been shown that the proposed multidimensional coded-modulation schemes based on OVQ-SCDs outperform corresponding counterparts and can be used to enable beyond 10 Pb/s serial optical transport over spatial division multiplexing (SDM) fibers as well as beyond 1 Pb/s transport over SMFs. The proposed OVQ-SCD-based hybrid multidimensional coded modulation scheme can simultaneously solve the problems related to the limited bandwidth of information-infrastructure, high energy consumption, and heterogeneity of network segments; while enabling elastic and dynamic bandwidth allocation.

Multidimensional Vector Quantization-Based Signal Constellation Design Enabling Beyond 1 Pb/s Serial Optical Transport Networks

In this paper, we propose a hybrid multidimensional coded-modulation (CM) scheme based on a new multidimensional signal constellation design as a solution to limited bandwidth and high energy consumption of information infrastructure. This multidimensional signal constellation, herewith called vector-quantization-based signal constellation design (VQ-SCD), is based on the vector quantization theory. The proposed scheme employs both the electrical basis functions (in the form of the prolate spheroidal wave functions) and the optical basis functions (in the form of polarization and spatial mode states) as optical basis functions. The proposed coded VQ-SCD scheme is the enabling technology for optical serial transport with bit rates exceeding 1000 Tb/s (1 Pb/s). In addition, the CM scheme we proposed allows for the adaptive, elastic, and dynamic allocation of the bandwidth. This fine granularity bandwidth manipulation is envisioned as a part of future software-defined optical networking.

Nonlinear Long-Term Prediction of Speech Based on Truncated Volterra Series

Previous studies of nonlinear prediction of speech have been mostly focused on short-term prediction. This paper presents long-term nonlinear prediction based on second-order Volterra filters. It will be shown that the presented predictor can outperform conventional linear prediction techniques in terms of prediction gain and “whiter” residuals.

An adaptive waveform coding algorithm and its application in speech coding

This paper proposes a novel waveform coding algorithm based on the forward adaptive technique with the goal to provide the overreaching of the signal to quantization noise ratio achievable by the coding solution designed according to G.711 standard. The novel algorithm performs frame-by-frame analysis of the input signal, according to which one of the two compandors, the restricted or the unrestricted one, is selected for the particular frame procession. The basic concept of the proposed algorithm is to enable a more preferable selection of the restricted compandor than the unrestricted one, since, in such a manner, an increase of the signal to quantization noise ratio can be provided. Since both the theoretical and the simulation results, which are obtained for the assumed input speech signal, indicate the performance improvement over the G.711 standard along with approximately 1 bit/sample compression, one can expect that the proposed algorithm will be effective in coding of signals, that as well as speech signals follow Laplacian distribution and have the time varying characteristics.

Asymptotic Analysis of Switched Uniform Polar Quantization for Memoryless Gaussian Source

This letter performs an asymptotic analysis of the switched uniform polar quantizer (SUPQ) composed of k asymptotically optimal unrestricted uniform polar quantizers designed for the memoryless Gaussian source. The closed-form formulas are derived for signal to quantization noise ratio (SQNR) and the number of phase levels of the quantizers constituting the SUPQ. It is studied how SQNR depends on the variance mismatch and the number of quantizers k. It is shown that with a log-uniform distribution of the variances for which the quantizers constituting the SUPQ are designed one can reduce the variance range of average-taking of SQNR.

Robust and switched nonuniform scalar quantization of Gaussian source in a wide dynamic range of power

In this paper, robust and switched nonuniform scalar quantization model is analyzed for the case when the power of an input signal varies in a wide range. This model of scalar quantization is used in order to give higher quality by increasing signal-to-quantization noise ratio (SNRQ) in a wide range of signal volumes (variances) with respect to its necessary robustness over a broad range of input variances. We observed μ-low compandor implementation to achieve compromise between high-rate digitalization and variance adaptation. Accurate estimate of the input signal variance is needed when finding the best compressor function for a compandor implementation. It enables quantizers to be adapted to the maximal amplitudes of input signals. In addition, we found the expression for distortion, which we used to estimate the suggested model. In the nature of optimizing parameters of this model, we derived conclusions about the possibilities of this switched quantization application in speech processing. We analized influence of codebook size and number on quality of transmission, and compared ITU-T G711 standard with our model. The main contribution of this model is increasing of quality and the possibility of his applying for digitalization of continuous signals in wide range.

Optimal polar image sampling

In this paper, a problem of efficient image sampling (deployment of image sensors) is considered. This problem is solved using techniques of two-dimensional quantization in polar coordinates, taking into account human visual system (HVS) and eye sensitivity function. The optimal radial compression function for polar quantization is derived. Optimization of the number of the phase levels for each amplitude level is done. Using optimal radial compression function and optimal number of phase levels for each amplitude level, optimal polar quantization is defined. Using deployment of quantization cells for the optimal polar quantization, deployment of image sensors is done, and therefore optimal polar image sampling is obtained. It is shown that our solution (the optimal polar sampling) has many advantages compared to presently used solutions, based on the log-polar sampling. The optimal polar sampling gives higher SNR (signal-to-noise ratio), compared to the log-polar sampling, for the same number of sensors. Also, the optimal polar sampling needs smaller number of sensors, to achieve the same SNR, compared to the log-polar sampling. Furthermore, with the optimal polar sampling, points in the image middle can be sampled, which is not valid for the log-polar sampling. This is very important since human eye is the most sensitive to these points, and therefore the optimal polar sampling gives better subjective quality.

Geometric piecewise uniform lattice vector quantization of the memoryless Gaussian source

The aim of this paper is to find a quantization technique that has low implementation complexity and asymptotic performance arbitrarily close to the optimum. More specifically, it is of interest to develop a new vector quantizer design procedure for a memoryless Gaussian source that yields vector quantizers with excellent performance and the structure required for fast quantization. To achieve this, we combined a fast lattice-encoding algorithm with a geometric approach to generate a model of a geometric piecewise-uniform lattice vector quantizer. Expressions for granular distortion and the optimal number of outputs points in each region were derived. Both exact and approximative asymptotic analyses were carried out. During this process, the constant probability density function of the input signal vector was kept inside the whole region. The analysis demonstrated the existence of piecewise-constant approximations to the input-vector probability density function, which is optimal for the proposed geometric piecewise-uniform vector quantizer. The considered quantization technique is near optimal for a memoryless Gaussian source. In other words, this paper proposes a method for a near-optimum, low-complex vector quantizer design based on probability density function discretization. The presented methodology gives a signal-to-quantization noise ratio that in some cases differs from the optimum by 0.1dB or less. Improvements of the considered model in performance and complexity over some of the existing techniques are also demonstrated.