The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform
Zoran Peric - One of the best experts on this subject based on the ideXlab platform.
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Coding Algorithm for Grayscale Images Based on Piecewise Uniform Quantizers
Informatica (lithuanian Academy of Sciences), 2020Co-Authors: Milan S. Savić, Zoran Peric, Milan R DincicAbstract:In this paper, a piecewise Uniform Quantizer for input samples with discrete amplitudes for Laplacian source is designed and analyzed, and its forward adaptation is done. This type of Quantizers is very often used in practice for the purpose of compression and coding of already quantized signals. It is shown that the design and the adaptation of Quantizers for discrete input samples are different from the design and the adaptation of Quantizers for continual input samples. A weighting function for PSQNR (peak signal-to-quantization noise ratio), which is obtained based on probability density function of variance of standard test images is introduced. Experiments are done, applying these Quantizers for compression of grayscale images. Experimental results are very well matched to the theoretical results, proving the theory. Adaptive piecewise Uniform Quantizer designed for discrete input samples gives for 9 to 20 dB higher PSQNR compared to the fixed piecewise Uniform Quantizer designed for discrete input samples. Also it is shown that the adaptive piecewise Uniform Quantizer designed for discrete input samples gives higher PSQNR for 1.46 to 3.45 dB compared the adaptive piecewise Uniform Quantizer designed for continual input samples, which proves that the discrete model is more appropriate for image quantization than continual model.
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design of forward adaptive Uniform Quantizer for discrete input samples for laplacian source
Elektronika Ir Elektrotechnika, 2015Co-Authors: Milan S Savic, Zoran Peric, Milan R DincicAbstract:In this paper, the problem of construction of the fixed and the forward adaptive Uniform Quantizer for input samples with discrete amplitude is analyzed, for Laplacian source. This Quantizer is very common in practice; therefore its analysis is very significant. It is shown that design and performances of the quantiazer for discrete input samples are very different, compared to Quantizer for continual input samples.
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multiproduct Uniform polar Quantizer
Radioengineering, 2015Co-Authors: Milan R Dincic, Zoran PericAbstract:The aim of this paper is to reduce the complexity of the unrestricted Uniform polar Quantizer (UUPQ), keeping its high performances. To achieve this, in this paper we propose the multiproduct Uniform polar quan- tizer (MUPQ), where several consecutive magnitude levels are joined in segments and within each segment the uni- form product quantization is performed (i.e. all levels within one segments have the same number of phase levels). MUPQ is much simpler for realization than UUPQ, but it achieves similar performances as UUPQ. Since MUPQ has low complexity and achieves much better performances than the scalar Uniform Quantizer, it can be widely used instead of scalar Uniform Quantizers to im- prove performances, for any signal with the Gaussian distribution.
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analysis of two stage Quantizer with embedded g 711 Quantizer and segmental Uniform Quantizer
Elektronika Ir Elektrotechnika, 2013Co-Authors: Zoran Peric, Jelena Nikolic, Jelena Lukic, Dragan DenicAbstract:In this paper a novel two-stage Quantizer with the embedded G.711 Quantizer is proposed for speech signal processing. The first processing stage, where the input signal is quantized with the G.711 Quantizer, is followed by the second stage where the segmental Uniform Quantizer performs the reduction of the quantization error introduced in the first stage. In this way higher signal quality, measured by signal to quantization noise ratio, is achieved in comparison with the G.711 Quantizer while no bit rate reduction is performed. Particularly, in the second stage two additional bits are introduced. Although the expected quality gain, as a result of increasing the overall bit rate for 2 bit/sample, is around 12 dB, the gain achieved with the proposed Quantizer is 14 dB. This additional quality gain of 2 dB proves the advantage of the proposed two-stage Quantizer. DOI: http://dx.doi.org/10.5755/j01.eee.19.2.1107
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design of novel piecewise Uniform scalar Quantizer for gaussian memoryless source
Radio Science, 2012Co-Authors: Lazar Velimirovic, Zoran Peric, Jelena NikolicAbstract:[1] In this paper we propose a novel piecewise Uniform scalar Quantizer model designed for a Gaussian memoryless source. The segment thresholds of this Quantizer model are equidistant while the number of reproduction levels inside segments is determined by optimizing the inner distortion and under the constraint of total number of reproduction levels. For the proposed Quantizer model the quantization efficiency and signal to quantization noise ratio (SQNR) is studied. On the basis of the comparison with the efficiency and SQNR of Uniform Quantizer advantages of the proposed Quantizer model are shown. Particularly, it is shown that with the proposed Quantizer the correlation between the input signal and the quantization error can be eliminated more than by using the Uniform Quantizer as well as that the proposed Quantizer provides higher level of SQNR.
Milan R Dincic - One of the best experts on this subject based on the ideXlab platform.
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Coding Algorithm for Grayscale Images Based on Piecewise Uniform Quantizers
Informatica (lithuanian Academy of Sciences), 2020Co-Authors: Milan S. Savić, Zoran Peric, Milan R DincicAbstract:In this paper, a piecewise Uniform Quantizer for input samples with discrete amplitudes for Laplacian source is designed and analyzed, and its forward adaptation is done. This type of Quantizers is very often used in practice for the purpose of compression and coding of already quantized signals. It is shown that the design and the adaptation of Quantizers for discrete input samples are different from the design and the adaptation of Quantizers for continual input samples. A weighting function for PSQNR (peak signal-to-quantization noise ratio), which is obtained based on probability density function of variance of standard test images is introduced. Experiments are done, applying these Quantizers for compression of grayscale images. Experimental results are very well matched to the theoretical results, proving the theory. Adaptive piecewise Uniform Quantizer designed for discrete input samples gives for 9 to 20 dB higher PSQNR compared to the fixed piecewise Uniform Quantizer designed for discrete input samples. Also it is shown that the adaptive piecewise Uniform Quantizer designed for discrete input samples gives higher PSQNR for 1.46 to 3.45 dB compared the adaptive piecewise Uniform Quantizer designed for continual input samples, which proves that the discrete model is more appropriate for image quantization than continual model.
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design of forward adaptive Uniform Quantizer for discrete input samples for laplacian source
Elektronika Ir Elektrotechnika, 2015Co-Authors: Milan S Savic, Zoran Peric, Milan R DincicAbstract:In this paper, the problem of construction of the fixed and the forward adaptive Uniform Quantizer for input samples with discrete amplitude is analyzed, for Laplacian source. This Quantizer is very common in practice; therefore its analysis is very significant. It is shown that design and performances of the quantiazer for discrete input samples are very different, compared to Quantizer for continual input samples.
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multiproduct Uniform polar Quantizer
Radioengineering, 2015Co-Authors: Milan R Dincic, Zoran PericAbstract:The aim of this paper is to reduce the complexity of the unrestricted Uniform polar Quantizer (UUPQ), keeping its high performances. To achieve this, in this paper we propose the multiproduct Uniform polar quan- tizer (MUPQ), where several consecutive magnitude levels are joined in segments and within each segment the uni- form product quantization is performed (i.e. all levels within one segments have the same number of phase levels). MUPQ is much simpler for realization than UUPQ, but it achieves similar performances as UUPQ. Since MUPQ has low complexity and achieves much better performances than the scalar Uniform Quantizer, it can be widely used instead of scalar Uniform Quantizers to im- prove performances, for any signal with the Gaussian distribution.
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Design of the adaptive piecewise Uniform scalar Quantizer with lossless coder and Golomb-Rice code on the output, for signals with Gaussian distribution
Journal of Communications Technology and Electronics, 2013Co-Authors: Milan R Dincic, Zoran H. PericAbstract:In this paper a new compression model for signals with Gaussian distribution is presented. It consists of a piecewise Uniform scalar Quantizer, a lossless coder and Golomb-Rice code. Forward adaptation of this proposed model is done and constant signal-to-quantization noise ratio ( SQNR ) in wide range of input variances is obtained. Joined design of lossy and lossless coder is done, achieving optimal performances. Our model has small complexity since all components of the model (piecewise Uniform Quantizer, lossless coder and Golomb-Rice code) are very simple for realization. As an example, this model is applied on the speech signal, and it is shown that the G.712 standard is satisfied with bit-rate decrease of 1.09 bits/sample, compared to the classic Uniform Quantizer.
Jelena Nikolic - One of the best experts on this subject based on the ideXlab platform.
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analysis of two stage Quantizer with embedded g 711 Quantizer and segmental Uniform Quantizer
Elektronika Ir Elektrotechnika, 2013Co-Authors: Zoran Peric, Jelena Nikolic, Jelena Lukic, Dragan DenicAbstract:In this paper a novel two-stage Quantizer with the embedded G.711 Quantizer is proposed for speech signal processing. The first processing stage, where the input signal is quantized with the G.711 Quantizer, is followed by the second stage where the segmental Uniform Quantizer performs the reduction of the quantization error introduced in the first stage. In this way higher signal quality, measured by signal to quantization noise ratio, is achieved in comparison with the G.711 Quantizer while no bit rate reduction is performed. Particularly, in the second stage two additional bits are introduced. Although the expected quality gain, as a result of increasing the overall bit rate for 2 bit/sample, is around 12 dB, the gain achieved with the proposed Quantizer is 14 dB. This additional quality gain of 2 dB proves the advantage of the proposed two-stage Quantizer. DOI: http://dx.doi.org/10.5755/j01.eee.19.2.1107
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design of novel piecewise Uniform scalar Quantizer for gaussian memoryless source
Radio Science, 2012Co-Authors: Lazar Velimirovic, Zoran Peric, Jelena NikolicAbstract:[1] In this paper we propose a novel piecewise Uniform scalar Quantizer model designed for a Gaussian memoryless source. The segment thresholds of this Quantizer model are equidistant while the number of reproduction levels inside segments is determined by optimizing the inner distortion and under the constraint of total number of reproduction levels. For the proposed Quantizer model the quantization efficiency and signal to quantization noise ratio (SQNR) is studied. On the basis of the comparison with the efficiency and SQNR of Uniform Quantizer advantages of the proposed Quantizer model are shown. Particularly, it is shown that with the proposed Quantizer the correlation between the input signal and the quantization error can be eliminated more than by using the Uniform Quantizer as well as that the proposed Quantizer provides higher level of SQNR.
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design of forward adaptive piecewise Uniform scalar Quantizer with optimized reproduction level distribution per segments
Elektronika Ir Elektrotechnika, 2012Co-Authors: Jelena Nikolic, Zoran Peric, Aleksandra Jovanovic, Dragan AnticAbstract:The problem we address in this paper is the design of nearly optimal scalar Quantizer in a wide variance range of the Laplacian input signals, using the piecewise Uniform Quantizers while restricting the class of Quantizers to be forward adaptive. Particularly, the design procedure of the piecewise Uniform Quantizer with an equidistant support region partion and the optimized reproduction level distribution per segments is presented along with the design procedure of its forward adaptive version. Reproduction level optimization is performed by optimizing the granular distortion of the proposed Quantizer using the method of the Lagrange multipliers. For the proposed model we study the influence of the segment number on the SQNR, as well as the SQNR robustness in a wide variance range. Since the results obtained for the assumed Laplacian distribution indicate the SQNR improvement over the G.711 standard, one can expect that the proposed Quantizer will be effective in the quantization of signals having the same distribution and the time varying characteristics. Ill. 4, bibl. 10 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.119.3.1356
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a simple improvement of Uniform Quantizer for gaussian source
Telecommunications Forum, 2011Co-Authors: Lazar Velimirovic, Zoran Peric, Jelena NikolicAbstract:This paper proposes a new manner for construction of a piecewise Uniform scalar Quantizer. Segments' tresholds of the proposed Quantizer model are equidistant, whereas the reproduction level number within the segment is different. The proposed Quantizer is designed for the Gaussian source. In order to improve performances of the proposed Quantizer model, the optimization of the segment tresholds is considered. The main advantage of the proposed Quantizer model is its low complexity.
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a method of designing an adaptive Uniform Quantizer for lpc coefficients quantization
Przegląd Elektrotechniczny, 2011Co-Authors: Zlatan Eskic, Zoran Peric, Jelena NikolicAbstract:This paper proposes a method of designing the adaptive Uniform Quantizer for frame by frame LPC coefficients quantization. The method firstly determines the support region thresholds of two Uniform Quantizers designated to quantize the minimal and the maximal value of LPC coefficients of each frame. Based on this, the Uniform Quantizer thresholds estimation for LPC coefficients quantization are provided. The results obtained by testing the proposed method in processing the speech signal from the TIMIT data base are presented and disscused in the paper. Streszczenie. W artykule zaproponowano metode zUniformowane go adaptacyjne kwantowania wspolczynnika LPC (linear prediction coders). Początkowo obliczana jest minimalna i maksymalna wartośc LPC dla kazdej ramki. Nastepnie zUniformowany wspolczynnik jest określany. Zaprezentowano test metody na przykladzie przetwarzania sygnalu mowy z bazy TIMIT. (Metoda projektowania adaptacyjnego kwantyzera LPC)
Hiroshi Shirai - One of the best experts on this subject based on the ideXlab platform.
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RWS - Effect of input-signal statistical property in delta-sigma modulator with non-Uniform quantization
2012 IEEE Radio and Wireless Symposium, 2012Co-Authors: Toru Kitayabu, Mao Hagiwara, Hiroyasu Ishikawa, Hiroshi ShiraiAbstract:In this paper, we provide the simulation results for the noise reduction in the delta-sigma modulator with a non-Uniform Quantizer under various types of input signals. The delta-sigma modulator employs a non-Uniform Quantizer and adjusts the spacing of the Quantizer referring to statistical properties of the input signal to the modulator. It reduces its quantization noise compared to the delta-sigma modulator with the Uniform Quantizer at the same number of output values. The simulation results showed that the amount of noise reduction varied from 0.15 dB to 4.9 dB depending on the input signal, while the effect is not limited by the architecture of delta-sigma modulators. Moreover, it is shown that the PAPR of the input signal cannot work as an indicator of the amount of the noise reduction.
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Delta-sigma modulator with non-Uniform quantization
2011 IEEE Radio and Wireless Symposium, 2011Co-Authors: Mao Hagiwara, Toru Kitayabu, Hiroyasu Ishikawa, Hiroshi ShiraiAbstract:In this paper, a novel delta-sigma modulator which has a non-Uniform Quantizer is proposed. The step sizes of a non-Uniform Quantizer are, in general, uniquely determined by the probability density function (PDF) of an input signal amplitude, while delta-sigma modulators usually become unstable when the non-Uniform Quantizer is directly applied to them. It is found that the proposed delta-sigma modulator reduces a quantization noise and works in a stable condition. To guarantee the stability of the delta-sigma modulator, the maximum output value of the Quantizer is fixed at a certain value. The rest of the output and threshold values of the non-Uniform Quantizer are determined by the PDF of the input signal amplitude. The simulation results show that the quantization noise power is reduced by up to 2.90 dB when the non-Uniform Quantizer is applied.
G K Venayagamoorthy - One of the best experts on this subject based on the ideXlab platform.
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comparison of non Uniform optimal Quantizer designs for speech coding with adaptive critics and particle swarm
IEEE Industry Applications Society Annual Meeting, 2005Co-Authors: G K VenayagamoorthyAbstract:This paper presents the design of a companding non-Uniform optimal scalar Quantizer for speech coding. The Quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a Uniform Quantizer. Two approaches are presented in this paper namely adaptive critic designs (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal Quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality (PESQ) standard.
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neural networks based non Uniform scalar Quantizer design with particle swarm optimization
IEEE Swarm Intelligence Symposium, 2005Co-Authors: G K VenayagamoorthyAbstract:Quantization is a crucial link in the process of digital speech communication. Non-Uniform Quantizer such as the logarithm Quantizers are commonly used in practice. In this paper, a companding non-Uniform Quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed Quantizer design is compared with the logarithm Quantizer for bit rates ranging from 3 to 8.
