cutcutcodec.core.analysis.video.complexity.dct¶

Compute a differenciable batched torch spacial dtc complexity.

Functions

spacial_dct(img[, threads])

Compute the spacial dct complexity for the image.

Details

cutcutcodec.core.analysis.video.complexity.dct.spacial_dct(img: Tensor, threads: int = 0) → Tensor[source]

Compute the spacial dct complexity for the image.

Parameters¶

imgarraylike: The Y[UV] images, of shape ([*batch], [1], height, width, [channels]). Only the Y component is used. It has to be in range [0, 1]. As there is no padding on the edges, the image must be at least 64x64 pixels.
threadsint, optional: Defines the number of threads. The value -1 means that the function uses as many calculation threads as there are cores. The default value (0) allows the same behavior as (-1) if the function is called in the main thread, otherwise (1) to avoid nested threads. Any other positive value corresponds to the number of threads used.

Returns¶

spacial_dctarraylike: The \(C_{sob} \in \mathbb{R}^+\) scalar for each image (of shape batch).

Examples¶

>>> import numpy as np
>>> from cutcutcodec.core.analysis.video.complexity import spacial_dct
>>> np.random.seed(0)
>>> img = np.random.random((720, 1080, 3))  # It could also be a torch array list...
>>> spacial_dct(img).round(1)
np.float64(1.4)
>>>