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vmaf_v6_bootstrap.py
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vmaf_v6_bootstrap.py
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feature_dict = {
'VMAF_feature': ['vif_scale0', 'vif_scale1', 'vif_scale2', 'vif_scale3',
'adm2', 'motion2',],
}
model_type = "BOOTSTRAP_LIBSVMNUSVR"
model_param_dict = {
# ==== preprocess: normalize each feature ==== #
# 'norm_type': 'none', # default: do nothing
'norm_type': 'clip_0to1', # rescale to within [0, 1]
# 'norm_type': 'clip_minus1to1', # rescale to within [-1, 1]
# 'norm_type': 'normalize', # rescale to mean zero and std one
# ==== postprocess: clip final quality score ==== #
# 'score_clip': None, # default: do nothing
'score_clip': [0.0, 100.0], # clip to within [0, 100]
# ==== postprocess: transform final quality score ==== #
'score_transform': {'p0':1.70674692, 'p1':1.72643844, 'p2':-0.00705305, 'out_gte_in':'true'}, # laptop vs. mobile transform
# ==== libsvmnusvr parameters ==== #
'gamma': 0.04,
'C': 4.0,
'nu': 0.9,
# ==== bootstrap parameters ==== #
'num_models': 21, # this leads to 20 bootstrapped models being trained
}