Howtosignalprocessing
- What are the properties of discrete wavelet transform?
- What is discrete wavelet transform used for?
- What are basics of wavelet transforms?
- What is difference between continuous wavelet transform and discrete wavelet transform?
What are the properties of discrete wavelet transform?
A discrete wavelet transform (DWT) is a transform that decomposes a given signal into a number of sets, where each set is a time series of coefficients describing the time evolution of the signal in the corresponding frequency band.
What is discrete wavelet transform used for?
The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding, to represent a discrete signal in a more redundant form, often as a preconditioning for data compression.
What are basics of wavelet transforms?
Wavelet transforms are mathematical tools for analyzing data where features vary over different scales. For signals, features can be frequencies varying over time, transients, or slowly varying trends. For images, features include edges and textures.
What is difference between continuous wavelet transform and discrete wavelet transform?
To summarize: The CWT and the discrete wavelet transforms differ in how they discretize the scale parameter. The CWT typically uses exponential scales with a base smaller than 2, for example 21/12 . The discrete wavelet transform always uses exponential scales with the base equal to 2.
