from ..utils import *
import numpy as np
import scipy.ndimage
import scipy.linalg
def est_params(frame, blk, sigma_nn):
h, w = frame.shape
sizeim = np.floor(np.array(frame.shape)/blk) * blk
sizeim = sizeim.astype(np.int)
frame = frame[:sizeim[0], :sizeim[1]]
#paired_products
temp = []
for u in range(blk):
for v in range(blk):
temp.append(np.ravel(frame[v:(sizeim[0]-(blk-v)+1), u:(sizeim[1]-(blk-u)+1)]))
temp = np.array(temp).astype(np.float32)
cov_mat = np.cov(temp, bias=1).astype(np.float32)
# force PSD
eigval, eigvec = np.linalg.eig(cov_mat)
Q = np.matrix(eigvec)
xdiag = np.matrix(np.diag(np.maximum(eigval, 0)))
cov_mat = Q*xdiag*Q.T
temp = []
for u in range(blk):
for v in range(blk):
temp.append(np.ravel(frame[v::blk, u::blk]))
temp = np.array(temp).astype(np.float32)
# float32 vs float64 difference between python2 and python3
# avoiding this problem with quick cast to float64
V,d = scipy.linalg.eigh(cov_mat.astype(np.float64))
V = V.astype(np.float32)
# Estimate local variance
sizeim_reduced = (sizeim/blk).astype(np.int)
ss = np.zeros((sizeim_reduced[0], sizeim_reduced[1]), dtype=np.float32)
if np.max(V) > 0:
# avoid the matrix inverse for extra speed/accuracy
ss = scipy.linalg.solve(cov_mat, temp)
ss = np.sum(np.multiply(ss, temp) / (blk**2), axis=0)
ss = ss.reshape(sizeim_reduced)
V = V[V>0]
# Compute entropy
ent = np.zeros_like(ss, dtype=np.float32)
for u in range(V.shape[0]):
ent += np.log2(ss * V[u] + sigma_nn) + np.log(2*np.pi*np.exp(1))
return ss, ent
def extract_info(frame1, frame2):
blk = 3
sigma_nsq = 0.1
sigma_nsqt = 0.1
model = SpatialSteerablePyramid(height=6)
y1 = model.extractSingleBand(frame1, filtfile="sp5Filters", band=0, level=4)
y2 = model.extractSingleBand(frame2, filtfile="sp5Filters", band=0, level=4)
ydiff = y1 - y2
ss, q = est_params(y1, blk, sigma_nsq)
ssdiff, qdiff = est_params(ydiff, blk, sigma_nsqt)
spatial = np.multiply(q, np.log2(1 + ss))
temporal = np.multiply(qdiff, np.multiply(np.log2(1 + ss), np.log2(1 + ssdiff)))
return spatial, temporal
[docs]def strred(referenceVideoData, distortedVideoData):
"""Computes Spatio-Temporal Reduced Reference Entropic Differencing (ST-RRED) Index. [#f1]_
Both video inputs are compared over frame differences, with quality determined by
differences in the entropy per subband.
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. Here C is only allowed to be 1.
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. Here C is only allowed to be 1.
Returns
-------
strred_array : ndarray
The ST-RRED results, ndarray of dimension ((T-1)/2, 4), where T
is the number of frames. Each row holds spatial score, temporal score,
reduced reference spatial score, and reduced reference temporal score.
strred : float
The final ST-RRED score if all blocks are averaged after comparing
reference and distorted data. This is close to full-reference.
strredssn : float
The final ST-RRED score if all blocks are averaged before comparing
reference and distorted data. This is the reduced reference score.
References
----------
.. [#f1] R. Soundararajan and A. C. Bovik, "Video Quality Assessment by Reduced Reference Spatio-temporal Entropic Differencing," IEEE Transactions on Circuits and Systems for Video Technology, April 2013.
"""
referenceVideoData = vshape(referenceVideoData)
distortedVideoData = vshape(distortedVideoData)
assert(referenceVideoData.shape == distortedVideoData.shape)
T, M, N, C = referenceVideoData.shape
assert C == 1, "strred called with videos containing %d channels. Please supply only the luminance channel" % (C,)
referenceVideoData = referenceVideoData[:, :, :, 0]
distortedVideoData = distortedVideoData[:, :, :, 0]
rreds = []
rredt = []
rredssn = []
rredtsn = []
for i in range(0, T-1, 2):
refFrame1 = referenceVideoData[i].astype(np.float32)
refFrame2 = referenceVideoData[i+1].astype(np.float32)
disFrame1 = distortedVideoData[i].astype(np.float32)
disFrame2 = distortedVideoData[i+1].astype(np.float32)
spatialRef, temporalRef = extract_info(refFrame1, refFrame2)
spatialDis, temporalDis = extract_info(disFrame1, disFrame2)
rreds.append(np.mean(np.abs(spatialRef - spatialDis)))
rredt.append(np.mean(np.abs(temporalRef - temporalDis)))
rredssn.append(np.abs(np.mean(spatialRef - spatialDis)))
rredtsn.append(np.abs(np.mean(temporalRef - temporalDis)))
rreds = np.array(rreds)
rredt = np.array(rredt)
rredssn = np.array(rredssn)
rredtsn = np.array(rredtsn)
srred = np.mean(rreds)
trred = np.mean(rredt)
srredsn = np.mean(rredssn)
trredsn = np.mean(rredtsn)
strred = srred * trred
strredsn = srredsn * trredsn
return np.hstack((rreds.reshape(-1, 1), rredt.reshape(-1, 1), rredssn.reshape(-1, 1), rredtsn.reshape(-1, 1))), strred, strredsn
