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Properties of the basic multiresolution scaling function

The first summary is given in four tables of the basic relationships and equations, primarily developed in Chapter: The Scaling Function and Scaling Coefficients, Wavelet and Wavelet Coefficients , for the scaling function φ ( t ) , scaling coefficients h ( n ) , and their Fourier transforms Φ ( ω ) and H ( ω ) for the multiplier M = 2 or two-band multiresolution system. The various assumptions and conditions are omitted in order to see the “big picture"and to see the effects of increasing constraints.

Properties of M = 2 Scaling Functions (SF) and their Fourier Transforms
Case Condition φ ( t ) Φ ( ω ) Signal Space
1 Multiresolution φ ( t ) = h ( n ) 2 φ ( 2 t - n ) Φ ( ω ) = 1 2 H ( ω 2 k ) distribution
2 Partition of 1 φ ( t - n ) = 1 Φ ( 2 π k ) = δ ( k ) distribution
3 Orthogonal φ ( t ) φ ( t - k ) d t = δ ( k ) | Φ ( ω + 2 π k ) | 2 = 1 L 2
5 SF Smoothness d ( ) φ d t < poly V j
6 SF Moments t k φ ( t ) d t = 0 Coiflets
Properties of M = 2 Scaling Coefficients and their Fourier Transforms
Case Condition h ( n ) H ( ω ) Eigenval.{ T }
1 Existence h ( n ) = 2 H ( 0 ) = 2
2 Fundamental h ( 2 n ) = h ( 2 n + 1 ) H ( π ) = 0 EV = 1
3 QMF h ( n ) h ( n - 2 k ) = δ ( k ) | H ( ω ) | 2 + | H ( ω + π ) | 2 = 2 EV 1
4 Orthogonal h ( n ) h ( n - 2 k ) = δ ( k ) | H ( ω ) | 2 + | H ( ω + π ) | 2 = 2 one EV = 1
L 2 Basis and H ( ω ) 0 , | ω | π / 3 others < 1
6 Coiflets n k h ( n ) = 0
Properties of M = 2 Wavelets (W) and their Fourier Transforms
Case Condition ψ ( t ) Ψ ( ω ) Signal Space
1 MRA ψ ( t ) = h 1 ( n ) 2 φ ( 2 t - n ) Ψ ( ω ) = 1 2 H 1 ( ω 2 k ) distribution
3 Orthogonal ϕ ( t ) ψ ( t - k ) d t = 0 L 2
3 Orthogonal ψ ( t ) ψ ( t - k ) d t = δ ( k ) L 2
5 W Moments t k ψ ( t ) d t = 0 poly n o t W j
Properties of M = 2 Wavelet Coefficients and their Fourier Transforms
Case Condition h 1 ( n ) H 1 ( ω ) Eigenval.{ T }
2 Fundamental h 1 ( n ) = 0 H 1 ( 0 ) = 0
3 Orthogonal h 1 ( n ) = ( - 1 ) n h ( 1 - n ) | H 1 ( ω ) | = | H ( ω + π ) |
3 Orthogonal h 1 ( n ) h 1 ( 2 m - n ) = δ ( m ) | H 1 ( ω ) | 2 + | H ( ω ) | 2 = 2
5 Smoothness n k h 1 ( n ) = 0 H ( ω ) = ( ω - π ) k H ˜ ( ω ) 1 , 1 2 , 1 4 ,

The different “cases" represent somewhat similar conditions for the stated relationships. For example, in Case 1, Table 1, themultiresolution conditions are stated in the time and frequency domains while in Table 2 the corresponding necessary conditionson h ( n ) are given for a scaling function in L 1 . However, the conditions are not sufficient unless general distributions areallowed. In Case 1, Table 3, the definition of a wavelet is given to span the appropriate multiresolution signal space butnothing seems appropriate for Case 1 in Table 4. Clearly the organization of these tables are somewhat subjective.

If we “tighten" the restrictions by adding one more linear condition, we get Case 2 which has consequences in Tables 1, 2, and 4 but does not guarantee anything better that adistribution. Case 3 involves orthogonality, both across scales and translations, so there are two rows for Case 3 in the tablesinvolving wavelets. Case 4 adds to the orthogonality a condition on the frequency response H ( ω ) or on the eigenvalues of the transition matrix to guarantee an L 2 basis rather than a tight frame guaranteed for Case 3. Cases 5 and 6concern zero moments and scaling function smoothness and symmetry.

In some cases, columns 3 and 4 are equivalent and others, they are not. In some categories, a higher numbered case assumes alower numbered case and in others, they do not. These tables try to give a structure without the details. It is useful torefer to them while reading the earlier chapters and to refer to the earlier chapters to see the assumptions and conditionsbehind these tables.

Types of wavelet systems

Here we try to present a structured list of the various classes of wavelet systems in terms of modification and generalizations of thebasic M = 2 system. There are some classes not included here because the whole subject is still an active research area, producing newresults daily. However, this list plus the table of contents, index, and references will help guide the reader through the maze. Therelevant section or chapter is given in parenthesis for each topic.

  • Signal Expansions [link]
    • General Expansion Systems [link]
    • Multiresolution Systems [link]
  • Multiresolution Wavelet Systems [link]
  • Length of scaling function filter [link]
    • Compact support wavelet systems
    • Infinite support wavelet systems
  • Orthogonality [link]
    • Orthogonal or Orthonormal wavelet bases
    • Semiorthogonal systems
    • Biorthogonal systems [link]
  • Symmetry
    • Symmetric scaling functions and wavelets [link] , [link]
    • Approximately symmetric systems [link]
    • Minimum phase spectral factorization systems [link]
    • General scaling functions
  • Complete and Overcomplete systems [link] , [link]
    • Frames
    • Tight frames
    • Redundant systems and transforms [link] , [link]
    • Adaptive systems and transforms, pursuit methods [link]
  • Discrete and continuous signals and transforms {analogous Fourier method} [link]
    • Discrete Wavelet Transform {Fourier series} [link]
    • Discrete-time Wavelet Transform {Discrete Fourier transforms} [link] , [link]
    • Continuous-time Wavelet Transform {Fourier transform or integral} [link]
  • Wavelet design [link]
    • Max. zero wavelet moments [Daubechies]
    • Max. zero scaling function moments
    • Max. mixture of SF and wavelet moments zero [Coifman] [link]
    • Max. smooth scaling function or wavelet [Heller, Lang, etc.]
    • Min. scaling variation [Gopinath, Odegard, etc.]
    • Frequency domain criteria
      • Butterworth [Daubechies]
      • least-squares, constrained LS, Chebyshev
    • Cosine modulated for M-band systems [link]
  • Descriptions [link]
    • The signal itself
    • The discrete wavelet transform (expansion coefficients)
    • Time functions at various scales or translations
    • Tiling of the time-frequency/scale plane [link]

Questions & Answers

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ninjadapaul
20/(×-6^2)
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I got X =-6
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The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
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Source:  OpenStax, Wavelets and wavelet transforms. OpenStax CNX. Aug 06, 2015 Download for free at https://legacy.cnx.org/content/col11454/1.6
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