def _post_process_result(cls, result):
# override Executor._post_process_result
def _strred(srred_trred):
srred, trred = srred_trred
try:
return srred * trred
except TypeError: # possible either srred or trred is None
return None
result = super(StrredFeatureExtractor, cls)._post_process_result(result)
# calculate refvar and disvar from ref1st, ref2nd, dis1st, dis2nd
srred_scores_key = cls.get_scores_key('srred')
trred_scores_key = cls.get_scores_key('trred')
strred_scores_key = cls.get_scores_key('strred')
srred_scores = result.result_dict[srred_scores_key]
trred_scores = result.result_dict[trred_scores_key]
# compute strred scores
# === Way One: consistent with VMAF framework, which is to multiply S and T scores per frame, then average
# strred_scores = map(_strred, zip(srred_scores, trred_scores))
# === Way Two: authentic way of calculating STRRED score: average first, then multiply ===
assert len(srred_scores) == len(trred_scores)
strred_scores = ListStats.nonemean(srred_scores) * ListStats.nonemean(trred_scores) * np.ones(len(srred_scores))
result.result_dict[strred_scores_key] = strred_scores
# validate
for feature in cls.DERIVED_ATOM_FEATURES:
assert cls.get_scores_key(feature) in result.result_dict
return result
def _post_process_result(cls, result):
# override Executor._post_process_result
def _strred(srred_trred):
srred, trred = srred_trred
try:
return srred * trred
except TypeError: # possible either srred or trred is None
return None
result = super(StrredFeatureExtractor, cls)._post_process_result(result)
# calculate refvar and disvar from ref1st, ref2nd, dis1st, dis2nd
srred_scores_key = cls.get_scores_key('srred')
trred_scores_key = cls.get_scores_key('trred')
strred_scores_key = cls.get_scores_key('strred')
srred_scores = result.result_dict[srred_scores_key]
trred_scores = result.result_dict[trred_scores_key]
# compute strred scores
# === Way One: consistent with VMAF framework, which is to multiply S and T scores per frame, then average
# strred_scores = map(_strred, zip(srred_scores, trred_scores))
# === Way Two: authentic way of calculating STRRED score: average first, then multiply ===
assert len(srred_scores) == len(trred_scores)
strred_scores = ListStats.nonemean(srred_scores) * ListStats.nonemean(trred_scores) * np.ones(len(srred_scores))
result.result_dict[strred_scores_key] = strred_scores
# validate
for feature in cls.DERIVED_ATOM_FEATURES:
assert cls.get_scores_key(feature) in result.result_dict
return result
def _post_process_result(cls, result):
# override Executor._post_process_result
def _strred(srred_trred):
srred, trred = srred_trred
if srred is not None and trred is not None:
return srred * trred
elif srred is None:
return trred
elif trred is None:
return srred
else:
return None
result = super(StrredOptFeatureExtractor,
cls)._post_process_result(result)
srred_scores_key = cls.get_scores_key('srred')
trred_scores_key = cls.get_scores_key('trred')
strred_scores_key = cls.get_scores_key('strred')
strred_all_same_scores_key = cls.get_scores_key('strred_all_same')
srred_scores = result.result_dict[srred_scores_key]
trred_scores = result.result_dict[trred_scores_key]
assert len(srred_scores) == len(trred_scores)
# === Way One: consistent with VMAF framework, which is to multiply S and T scores per frame, then average
strred_scores = list(map(_strred, zip(srred_scores, trred_scores)))
# === Way Two: authentic way of calculating STRRED score: average first, then multiply ===
strred_all_same_scores = ListStats.nonemean(
srred_scores) * ListStats.nonemean(trred_scores) * np.ones(
len(srred_scores))
result.result_dict[strred_all_same_scores_key] = strred_all_same_scores
result.result_dict[strred_scores_key] = strred_scores
# validate
for feature in cls.DERIVED_ATOM_FEATURES:
assert cls.get_scores_key(feature) in result.result_dict
return result