cutcutcodec.core.analysis.video.quality.ssim

cutcutcodec.core.analysis.video.quality.ssim(ref: Tensor, dis: Tensor, *args, stride: int = 1, **kwargs) → Tensor[source]

Compute the Structural similarity index measure of 2 images.

Parameters

ref, disarraylike: The 2 images to be compared, of shape ([*batch], height, width, channels). Supported types are float32 and float64.
data_rangefloat, default=1.0: The data range of the input image (difference between maximum and minimum possible values).
weightsiterable[float], optional: The relative weight of each channel. By default, all channels have the same weight.
sigmafloat, default=1.5: The standard deviation of the gaussian. It has to be strictely positive.
strideint, default=1: The stride of the convolving kernel.
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

ssimarraylike: The ponderated structural similarity index measure of each layers.

Notes

It is optimized for C contiguous tensors.
If device is cpu, gradient is not required and stride != 1, a fast C code is used.

Examples

>>> import numpy as np
>>> from cutcutcodec.core.analysis.video.quality import ssim
>>> np.random.seed(0)
>>> ref = np.random.random((720, 1080, 3))  # It could also be a torch array list...
>>> dis = 0.8 * ref + 0.2 * np.random.random((720, 1080, 3))
>>> ssim(ref, dis).round(2)
np.float64(0.95)
>>>