Source code for skvideo.measure.mse
from ..utils import *
import numpy as np
import scipy.ndimage
[docs]def mse(referenceVideoData, distortedVideoData):
"""Computes mean-squared error (MSE).
Both video inputs are compared frame-by-frame to obtain T
MSE measurements.
Parameters
----------
referenceVideoData : ndarray
Reference video, ndarray of dimension (T, M, N, C), (T, M, N), (M, N, C), or (M, N),
where T is the number of frames, M is the height, N is width,
and C is number of channels.
distortedVideoData : ndarray
Distorted video, ndarray of dimension (T, M, N, C), (T, M, N), (M, N, C), or (M, N),
where T is the number of frames, M is the height, N is width,
and C is number of channels.
Returns
-------
mse_array : ndarray
The mse results, ndarray of dimension (T,), where T
is the number of frames
"""
referenceVideoData = vshape(referenceVideoData)
distortedVideoData = vshape(distortedVideoData)
assert(referenceVideoData.shape == distortedVideoData.shape)
T, M, N, C = referenceVideoData.shape
assert C == 1, "mse called with videos containing %d channels. Please supply only the luminance channel" % (C,)
scores = np.zeros(T, dtype=np.float32)
for t in range(T):
referenceFrame = referenceVideoData[t].astype(np.float32)
distortedFrame = distortedVideoData[t].astype(np.float32)
mse = np.mean((referenceFrame - distortedFrame)**2)
scores[t] = mse
return scores