Stockwell Transform and its Modifications in Signal Processing Courses: Comparison and Features

It is well known that signal transformations are very powerful and popular methods for signal and image processing. Because of its ability to decompose signals, signal analysis on form level is enabled. Therefore, there are many more or less known signal transformations in the scientific community....

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Main Authors: Maksimovic, Snjezana, Gajic, Slavica
Format: Conference Proceeding
Language:English
Subjects:
Discrete cosine transforms
Discrete wavelet transforms
Image coding
Image resolution
signal and image processing
Stockwell transformation
Time-frequency analysis
transformations in teaching
Transforms
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Gajic, Slavica
description It is well known that signal transformations are very powerful and popular methods for signal and image processing. Because of its ability to decompose signals, signal analysis on form level is enabled. Therefore, there are many more or less known signal transformations in the scientific community. The most popular ones are Fourier transform, short time Fourier transform, Wavelet transform, Discrete cosine transform and those transforms are commonly mentioned in academic literature. On the other hand, Stockwell transform has self-adjusted window size, while fractional Stockwell transform improves the flexibility of time-frequency analysis and energy of signal spectra. Because of the above, in this paper Stockwell transform advantages and applications in the signal and image processing area are analyzed. Its features and parity to the other transformations is shown. Also, the most commonly used fractional transformations that improve standard transformations, because their ability of rotation, were listed.
doi_str_mv 10.1109/INFOTEH57020.2023.10094190
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subjects Discrete cosine transforms
Discrete wavelet transforms
Image coding
Image resolution
signal and image processing
Stockwell transformation
Time-frequency analysis
transformations in teaching
Transforms
title Stockwell Transform and its Modifications in Signal Processing Courses: Comparison and Features
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