Digital coding of waveforms. Principles and applications to speech and video

Jayant, N. S.; Noll, P.

doi:10.1016/0165-1684(85)90053-2

Cited by 653 publications

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“…This procedure, in contrast to that of the nonuniform quantizer models does not require the full search of the quantizer code book [Jayant and Noll, 1984;Hanzo et al, 2007]. The G.711 standard defines a symmetric PUSQ by 8 bits of resolution and L = 8 positive segments increased in length by a factor of 2 for each successive segment having 16 cells [Jayant and Noll, 1984]. The G.711 quantizer is widely used in many systems, especially in PSTN (public switched telephone network).…”

Section: Introductionmentioning

confidence: 99%

“…One of the main goals when designing quantizers is to determine the reproduction levels and the partition of the support region into cells, such as to provide the minimum possible distortion for a fixed number of quantization levels N, or a fixed resolution R = log 2 N [Jayant and Noll, 1984;Hanzo et al, 2007]. A quantizer support region can be divided in a variety of ways.…”

Section: Introductionmentioning

confidence: 99%

“…A quantizer support region can be divided in a variety of ways. If a quantizer support region consists of several segments, each of which contains several quantization cells and reproduction levels corresponding to a uniform quantizer, the quantizer is a piecewise uniform one [Jayant and Noll, 1984;Hanzo et al, 2007] (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…[7] The simplest scalar quantizer design and its realization refer to the uniform scalar quantization [Jayant and Noll, 1984]. In the work of Thompson et al [2007] the uniform quantization of a Gaussian source has been considered (see Figure 2).…”

Section: Introductionmentioning

confidence: 99%

“…The G.711 quantizer is widely used in many systems, especially in PSTN (public switched telephone network). It is based on a piecewise uniform approximation to the A-law or m-law compressor characteristics [Jayant and Noll, 1984].…”

Section: Introductionmentioning

confidence: 99%

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Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source

Velimirović¹,

Perić²,

Nikolić³

2012

Radio Science

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[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.Citation: Velimirović, L., Z. Perić, and J. Nikolić (2012), Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source, Radio Sci., 47, RS2005,

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source

Velimirović¹,

Perić²,

Nikolić³

2012

Radio Science

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Audio Signal Processing and Coding

Spanias¹,

Painter²,

Atti³

2005

140

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Feature Extraction

Martínez-Murcia

Górriz

Ramı́rez

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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Feature extraction is a procedure aimed at selecting and transforming a data set in order to increase the performance of a pattern recognition or machine learning system. Nowadays, since the amount of data available and its dimension is growing exponentially, it is a fundamental procedure to avoid overfitting and the curse of dimensionality, while, in some cases, allowing a interpretative analysis of the data. The topic itself is a thriving discipline of study, and it is difficult to address every single feature extraction algorithm. Therefore, we provide an overview of the topic, introducing widely used techniques, while at the same time presenting some domain-specific feature extraction algorithms. Finally, as a case, study, we will illustrate the vastness of the field by analysing the usage and impact of feature extraction in neuroimaging.

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Digital coding of waveforms. Principles and applications to speech and video

Cited by 653 publications

References 0 publications

Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source

Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source

Audio Signal Processing and Coding

Feature Extraction

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