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Classical signal processing deals with one-dimensional discrete transforms. Other application areas, such as
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Transforms between a discrete domain and a continuous domain are not discrete transforms. For example, the
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35:, of signals between discrete domains, such as between discrete time and discrete frequency.
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104:, from continuous time to discrete frequency, are outside the class of discrete transforms.
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used in signal processing have their discrete counterparts. For example, for the
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181:(2nd ed.). Cambridge University Press. p. 332.
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of signals, discrete transforms play important role in
100:, from discrete time to continuous frequency, and the
129:List of transforms § Discrete transforms
8:
16:Mathematical transform on discrete signals
154:. McGraw-Hill Professional. p. 147.
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7:
14:
94:discrete-time Fourier transform
77:(DCT) is the most widely used
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178:Signal coding and processing
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148:Jerry C. Whitaker (2001).
117:high-definition television
87:discrete wavelet transform
48:discrete Fourier transform
81:compression algorithm in
75:discrete cosine transform
46:the counterpart is the
29:mathematical transforms
175:Graham Wade (1994).
151:Television receivers
209:Discrete transforms
40:integral transforms
25:discrete transforms
85:, followed by the
188:978-0-521-42336-6
161:978-0-07-138042-3
67:digital filtering
55:spectral analysis
44:Fourier transform
33:linear transforms
21:signal processing
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109:image processing
79:transform coding
63:signal detection
59:data compression
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113:computer vision
53:In addition to
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102:Fourier series
73:analysis. The
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38:Many common
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98:Z-transform
71:correlation
135:References
203:Category
123:See also
96:and the
31:, often
89:(DWT).
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183:ISBN
156:ISBN
69:and
27:are
19:In
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