$V_n$ usually denotes the span of a certain scaling function, $\phi$, i.e. How can I predict the number of coefficients, and what are some good resources for gaining better understanding of why? I have no idea where 6, 6 and 9 come from, and they change depending on the level I specify (not even sure what it means to specify a level) and of course the input size. My problem is when I use the next Daubechies (referred to as 'db2' in the toolbox, which is called the D4), and I get V1, W1, W2 The V1 gives me the scaling function and the W1-W5 wavelets of different scale and dilation. It also provides code for more advanced wavelet transforms, so you can even explore alternative techniques. both matlab implementation and tutorial It does not require the Wavelet Toolbox, but it probably requires the Signal Processing Toolbox (not sure about the Image Processing Toolbox). Let's say my input function has 16 datapoints, if I use Haar, what I get from a multilevel decomposition ( wavedec) is something like this (the number of shifts in brackets): V1, W1, W2, W3, W4 It covers the separable 1D, 2D and 3D cases. I started by implementing it using Haar wavelets, which gave correct results and I understand exactly how it works. I am using Daubechies wavelets to describe a 1D function and I'm using PyWavelets to implement it (which is analogous to the MATLAB toolbox). I am wondering about the correlation between input size and number of coefficients given by a discrete wavelet transform.
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