matlab - Number of Daubechies coefficients

I am wondering about the correlation between input size and number of coefficients given by a discrete wavelet transform.

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 started by implementing it using Haar wavelets, which gave correct results and I understand exactly how it works. 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[1], W1[1], W2[2], W3[4], W4[8]

This is all well and good. The V1 gives me the scaling function and the W1-W5 wavelets of different scale and dilation. 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[6], W1[6], W2[9]

I lose all my intuition. 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. How can I predict the number of coefficients, and what are some good resources for gaining better understanding of why?

Thanks!

EDIT: Clarification on V and W:

$V_n$ usually denotes the span of a certain scaling function, $\phi$, i.e. $\{\phi_{n,k}\}$, where $k$ is the shift and $n$ the scaling. $W_n$ is the same except for the wavelet function, $\psi$. I might have abused the notation a bit by referring to the vectors of coefficients by V and W though.

EDIT2: Code

Here is the MATLAB code to produce the above:

>> [C, L] = wavedec(1:16, 4, 'db1'); L
L = 
     1     1     2     4     8    16
>> [C, L] = wavedec(1:16, 2, 'db2'); L
L =
     6     6     9    16

I actually used PyWavelets, where it looked like this:

>>> import pywt
>>> map(len, pywt.wavedec(range(16), 'db1')) # defaults to level = 4
[1, 1, 2, 4, 8]
>>> map(len, pywt.wavedec(range(16), 'db2')) # defaults to level = 2
[6, 6, 9]

Answer

According to MATLAB documentation on wavedec,

The length of each filter is equal to $2N$. If n = length(s), the signals $F$ and $G$ are of length $n + 2N −1$ and the coefficients $cA_1$ and $cD_1$ are of length

$$\text{floor}\left( \frac{n-1}{2} \right) + N$$

Here, $n=16$ is the length of your signal, and $N=2$ is the Daubechies number.

Putting all that together, your detail coefficients at the second level should be of length

$$\text{floor}\left( \frac{16-1}{2} \right) + 2 = 7+2 = 9.$$

At the second level, you coefficients should be of length

$$\text{floor}\left( \frac{9-1}{2} \right) + 2 = 4+2 = 6.$$

If you're wondering why this must be the case, imagine the filtering-decimation procedure. Scaling and wavelet function for $\text{db}m$ wavelets are of length $2m$. When you convolve length $n$ signal with length $2m$ signal, you get back a signal of length $l_0=n+2m-1$. If you take every second sample of this resulting signal, you get back something of length $l_1 = \frac{n+2m-1}{2} = \frac{l_0}{2}$. Of course, if $l_0$ is odd ($n$ is even), then we cannot split the signal exactly into two parts. With clever mathematics that I will not go into here, you can show that this last non-paired coefficient is redundant anyway (carries no information that we don't yet know), so you can just omit it. Therefore, we will always have the resulting decimated signal of length

$$l_1 = \text{floor}\left( \frac{l_0}{2} \right) = \text{floor}\left( \frac{n+2N-1}{2} \right) = \text{floor}\left( \frac{n-1}{2} \right)+N.$$