tf.signal.idct
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Computes the 1D Inverse Discrete Cosine Transform (DCT) of input.
tf.signal.idct(
input, type=2, n=None, axis=-1, norm=None, name=None
)
Currently Types I, II, III, IV are supported. Type III is the inverse of
Type II, and vice versa.
Note that you must re-normalize by 1/(2n) to obtain an inverse if norm is
not 'ortho'. That is:
signal == idct(dct(signal)) * 0.5 / signal.shape[-1].
When norm='ortho', we have:
signal == idct(dct(signal, norm='ortho'), norm='ortho').
Args |
|---|
input
|
A [..., samples] float32/float64 Tensor containing the
signals to take the DCT of.
|
type
|
The IDCT type to perform. Must be 1, 2, 3 or 4.
|
n
|
For future expansion. The length of the transform. Must be None.
|
axis
|
For future expansion. The axis to compute the DCT along. Must be -1.
|
norm
|
The normalization to apply. None for no normalization or 'ortho'
for orthonormal normalization.
|
name
|
An optional name for the operation.
|
Returns |
|---|
A [..., samples] float32/float64 Tensor containing the IDCT of
input.
|
Raises |
|---|
ValueError
|
If type is not 1, 2 or 3, n is not None,axisis
not-1, ornormis notNoneor'ortho'`.
|
Equivalent to scipy.fftpack.idct
for Type-I, Type-II, Type-III and Type-IV DCT.
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.signal.idct\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/signal/dct_ops.py#L212-L256) |\n\nComputes the 1D [Inverse Discrete Cosine Transform (DCT)](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Inverse_transforms) of `input`.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.signal.idct`](https://www.tensorflow.org/api_docs/python/tf/signal/idct), [`tf.compat.v1.spectral.idct`](https://www.tensorflow.org/api_docs/python/tf/signal/idct)\n\n\u003cbr /\u003e\n\n tf.signal.idct(\n input, type=2, n=None, axis=-1, norm=None, name=None\n )\n\nCurrently Types I, II, III, IV are supported. Type III is the inverse of\nType II, and vice versa.\n\nNote that you must re-normalize by 1/(2n) to obtain an inverse if `norm` is\nnot `'ortho'`. That is:\n`signal == idct(dct(signal)) * 0.5 / signal.shape[-1]`.\nWhen `norm='ortho'`, we have:\n`signal == idct(dct(signal, norm='ortho'), norm='ortho')`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|-----------------------------------------------------------------------------------------------------|\n| `input` | A `[..., samples]` `float32`/`float64` `Tensor` containing the signals to take the DCT of. |\n| `type` | The IDCT type to perform. Must be 1, 2, 3 or 4. |\n| `n` | For future expansion. The length of the transform. Must be `None`. |\n| `axis` | For future expansion. The axis to compute the DCT along. Must be `-1`. |\n| `norm` | The normalization to apply. `None` for no normalization or `'ortho'` for orthonormal normalization. |\n| `name` | An optional name for the operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `[..., samples]` `float32`/`float64` `Tensor` containing the IDCT of `input`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|--------------------------------------------------------------------------------------------------------|\n| `ValueError` | If `type` is not `1`, `2` or `3`, `n` is not `None,`axis`is not`-1`, or`norm`is not`None`or`'ortho'\\`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nscipy compatibility\n-------------------\n\n\u003cbr /\u003e\n\nEquivalent to [scipy.fftpack.idct](https://docs.scipy.org/doc/scipy-1.4.0/reference/generated/scipy.fftpack.idct.html)\nfor Type-I, Type-II, Type-III and Type-IV DCT.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e"]]
View source on GitHub