There are many kind of wavelet transform, and I think the names are quite confusing.

For instance, there are continuous and discrete wavelet transforms, in which, the “continuous” and “discrete” are for the wavelet parameters, not for the “data” itself. Therefore, for discrete data, there are “continuous” and “discrete” wavelet transforms, and for function, there are also “continuous” and “discrete” wavelet transforms.

In here, we will focus on discrete wavelet transform for function first. This discrete wavelet transform is also called as wavelet series, which express a compact support function into series of wavelet.

For simplicity, we also focus on orthonormal wavelet.

As the wavelet span the entire space, any compact function can be expressed as

where are integer.

Now, we move to discrete data discrete wavelet transform. The data is discrete, we can imagine only points are known with finite .

the integration becomes a finite sum.

Without loss of generality, we can set , and then the time axis becomes an integer number axis. We found that as the wavelet can only be expand, not shrink. Because there are finite number of data point, i.e. , .

However, this double summation for each is very time consuming. There is a Fast Discrete Wavelet Transform. Before we continuous, we must study the wavelet.

From the last post, we know that the scaling function that generate a MRA must be:

, where are integer. The set of shifted scaling function span a space . For the wavelet,

The set of shifted wavelet span a space , so that , so that

Since the wavelet is generated from the scaling function, we expect the coefficient of and are related. In fact, the relationship for orthonormal scaling function and wavelet is

I will continuous later….. very tired….