import numpy as np
import scipy.fftpack
import scipy.io
import scipy.misc
import scipy.ndimage
import scipy.stats
from ..utils import *
def _extract_subband_feats(mscncoefs):
# alpha_m, = extract_ggd_features(mscncoefs)
alpha_m, sigma_sq = ggd_features(mscncoefs.copy())
pps1, pps2, pps3, pps4 = paired_product(mscncoefs)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
alpha_m, sigma_sq,
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2, # (D2)
])
[docs]def brisque_features(videoData):
"""Computes Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE) features. [#f1]_ [#f2]_
Since this is a referenceless image quality algorithm, only 1 video is needed. This function
provides the raw features extracted per frame.
Parameters
----------
videoData : 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.
Returns
-------
features : ndarray
A matrix of shape (T, 36) of the computed BRISQUE features.
References
----------
.. [#f1] A. Mittal, A. K. Moorthy and A. C. Bovik, "No-Reference Image Quality Assessment in the Spatial Domain" IEEE Transactions on Image Processing, 2012.
.. [#f2] A. Mittal, A. K. Moorthy and A. C. Bovik, "Referenceless Image Spatial Quality Evaluation Engine," 45th Asilomar Conference on Signals, Systems and Computers , November 2011.
"""
videoData = vshape(videoData)
T, M, N, C = videoData.shape
assert C == 1, "brisque_features called with video having %d channels. Please supply only the luminance channel." % (C,)
feats = np.zeros((T, 36), dtype=np.float32)
for i in range(T):
full_scale = videoData[i, :, :, 0].astype(np.float32)
half_scale = scipy.misc.imresize(full_scale, 0.5, interp='bicubic', mode='F')
full_scale, _, _ = compute_image_mscn_transform(full_scale)
half_scale, _, _ = compute_image_mscn_transform(half_scale)
feats[i, 18*0:18*1] = _extract_subband_feats(full_scale)
feats[i, 18*1:18*2] = _extract_subband_feats(half_scale)
return feats
