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Given a mother scaling function φ t 2 —the choice of which will be discussed later—let us construct scaling functions at"coarseness-level- k" and "shift- n " as follows: φ k , n t 2 k 2 φ 2 k t n . Let us then use V k to denote the subspace defined by linear combinations of scaling functions at the k th level: V k span φ k , n t n . In the Haar system, for example, V 0 and V 1 consist of signals with the characteristics of x 0 t and x 1 t illustrated in .

We will be careful to choose a scaling function φ t which ensures that the following nesting property is satisfied: V 2 V 1 V 0 V -1 V -2 coarse detailed In other words, any signal in V k can be constructed as a linear combination of more detailed signals in V k 1 . (The Haar system gives proof that at least one such φ t exists.)

The nesting property can be depicted using the set-theoretic diagram, , where V 1 is represented by the contents of the largest egg (which includes the smaller two eggs), V 0 is represented by the contents of the medium-sized egg (which includes the smallest egg), and V 1 is represented by the contents of the smallest egg.

Going further, we will assume that φ t is designed to yield the following three important properties:

  • φ k , n t n constitutes an orthonormal basis for V k ,
  • V 0 (contains no signals).
    While at first glance it might seem that V should contain non-zero constant signals ( e.g. , x t a for a ), the only constant signal in 2 , the space of square-integrable signals, is the zero signal.
  • V 2 (contains all signals).
Because φ k , n t n is an orthonormal basis, the best (in 2 norm) approximation of x t 2 at coarseness-level- k is given by the orthogonal projection,
x k t n c k , n φ k , n t
c k , n φ k , n t x t

We will soon derive conditions on the scaling function φ t which ensure that the properties above are satisfied.

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Source:  OpenStax, Dspa. OpenStax CNX. May 18, 2010 Download for free at http://cnx.org/content/col10599/1.5
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