Title
Projektovanje kvantizera u algoritmima za kompresiju signala
Creator
Simić, Nikola B.
Copyright date
2019
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
Dozvoljavate samo preuzimanje i distribuciju dela, ako/dok se pravilno naznačava ime autora, bez ikakvih promena dela i bez prava komercijalnog korišćenja dela. Ova licenca je najstroža CC licenca. Osnovni opis Licence: http://creativecommons.org/licenses/by-nc-nd/3.0/rs/deed.sr_LATN. Sadržaj ugovora u celini: http://creativecommons.org/licenses/by-nc-nd/3.0/rs/legalcode.sr-Latn
Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 27.12.2019.
Other responsibilities
mentor
Perić, Zoran
član komisije
Jovanović, Aleksandra
član komisije
Nikolić, Jelena
član komisije
Savić, Milan
član komisije
Ilić, Anđelija
Academic Expertise
Prirodno-matematičke nauke
Academic Title
-
University
Univerzitet u Nišu
Faculty
Elektronski fakultet
Group
Katedra za telekomunikacije
Alternative title
The designing of quantizers in signal compression algorithms
Publisher
[N. B. Simić]
Format
XIII, 181 list
description
Biografija autora: list 177;
Biobibliografija: listovi 171-176;
Bibliografija: listovi 157-170.
description
Telecommunications
Abstract (en)
Signal compression algorithms represent an indispensable
element in many modern digital signal processing systems, especially
in multimedia systems where a large amount of data is transferred to a
large number of users, whereby it is not of interest to reconstruct the
signal without any loss of information at the receiveing end. Generally
speaking, signal digitization is performed in three steps – sampling,
quantization and encoding.
From the standpoint of the development of signal coding and
compression algorithms, the most significant step is quantization,
which performs discretization of signal amplitudes. The designing of
quantizers is not uniquely determined and it depends on the nature of
the input signal, the desired quality of the reconstructed signal at the
receiving end, as well as the complexity that affects the processing time
and the desired compression ratio. Although a large number of
quantizer types have been developed so far, it can be said that the area
is still insufficiently explored and that there is room for contributions.
Signal processing is usually performed in the time or spatial domain,
and the most commonly used type of quantizer is a scalar uniform
quantizer due to its simplicity. However, advanced coding and
compression algorithms use more complex robust and adaptive
quantization techniques, they often perform signal transformation into
a frequency domain and there is an increasing popularity of utilizing
various prediction and machine learning techniques.
In this dissertation, an analysis of some of the popular
quantization techniques in modern coding and compression algorithms
for both continuous and discrete input signals is presented and several
hybrid models are proposed in order to obtain some novel lowcomplexity
solutions that provide medium and high compression
ratios. The experiments are performed by processing a set of natural
signals and the representative examples taken are a test speech signal
in the case of continuous signals, as well as a set of standard
monochromatic images in the case of discrete signals. In addition,
Monte Carlo simulations are used to validate some of the developed
theoretical models. Performance estimation is performed using
objective measures and a theoretical model for performance estimation
is developed in the case of the proposed modified block truncation
coding algorithm.
Authors Key words
Kvantizacija, Kompresija, Kodovanje izvora informacija,
Transformaciono kodovanje, Diferencijalno kodovanje, Adaptacija,
Modelovanje, Slika, Govor
Authors Key words
Quantization, Compression, Source coding, Transform coding,
Differential coding, Adaptation, Modelling, Image, Speech
Classification
(621.391+621.394.14):004
Subject
T 121
Type
Tekst
Abstract (en)
Signal compression algorithms represent an indispensable
element in many modern digital signal processing systems, especially
in multimedia systems where a large amount of data is transferred to a
large number of users, whereby it is not of interest to reconstruct the
signal without any loss of information at the receiveing end. Generally
speaking, signal digitization is performed in three steps – sampling,
quantization and encoding.
From the standpoint of the development of signal coding and
compression algorithms, the most significant step is quantization,
which performs discretization of signal amplitudes. The designing of
quantizers is not uniquely determined and it depends on the nature of
the input signal, the desired quality of the reconstructed signal at the
receiving end, as well as the complexity that affects the processing time
and the desired compression ratio. Although a large number of
quantizer types have been developed so far, it can be said that the area
is still insufficiently explored and that there is room for contributions.
Signal processing is usually performed in the time or spatial domain,
and the most commonly used type of quantizer is a scalar uniform
quantizer due to its simplicity. However, advanced coding and
compression algorithms use more complex robust and adaptive
quantization techniques, they often perform signal transformation into
a frequency domain and there is an increasing popularity of utilizing
various prediction and machine learning techniques.
In this dissertation, an analysis of some of the popular
quantization techniques in modern coding and compression algorithms
for both continuous and discrete input signals is presented and several
hybrid models are proposed in order to obtain some novel lowcomplexity
solutions that provide medium and high compression
ratios. The experiments are performed by processing a set of natural
signals and the representative examples taken are a test speech signal
in the case of continuous signals, as well as a set of standard
monochromatic images in the case of discrete signals. In addition,
Monte Carlo simulations are used to validate some of the developed
theoretical models. Performance estimation is performed using
objective measures and a theoretical model for performance estimation
is developed in the case of the proposed modified block truncation
coding algorithm.
“Data exchange” service offers individual users metadata transfer in several different formats. Citation formats are offered for transfers in texts as for the transfer into internet pages. Citation formats include permanent links that guarantee access to cited sources. For use are commonly structured metadata schemes : Dublin Core xml and ETUB-MS xml, local adaptation of international ETD-MS scheme intended for use in academic documents.