mendevi.models.dnn

Predict the energy and the quality based on a neuronal network.

Classes

DNN(*args, **kwargs)	Deep neuronal network predictive model.
DNN_DecodeEnergy(*args, **kwargs)	Deep neuronal network for the video encoding energy prediction.
DNN_EncodeEnergy(*args, **kwargs)	Deep neuronal network for the video encoding energy prediction.
DNN_Quality(*args, **kwargs)	Deep neuronal network for the video quality prediction.

Details

class mendevi.models.dnn.DNN(*args: tuple, **kwargs: dict)

Deep neuronal network predictive model.

Initialise the model.

Parameters

titlestr, optional

The model title.

**kwargsdict

Includes the following fields.

sourcesstr

All sources for the model, the conference paper, the authors, etc.

input_labelslist[str]

The name of all input parameters. The possibles values are mendevi.plot.axis.Name.

output_labelslist[str]

The name of all output parameters. The possibles values are mendevi.plot.axis.Name.

aggregationlist[str]

Specifies the list of parameters that the model will not interpolate. By default, this list consists of the subset of discrete parameters from input_labels. For example, if you provide an empty list, a single instance of the model will be trained on all parameters.

class mendevi.models.dnn.DNN_DecodeEnergy(*args: tuple, **kwargs: dict)

Deep neuronal network for the video encoding energy prediction.

Examples

>>> from mendevi.models.dnn import DNN_DecodeEnergy
>>> model = DNN_DecodeEnergy().fit("svtav1_vs_rav1e_vs_aom.db", table="t_dec_decode")
>>> model.validate("svtav1_vs_rav1e_vs_aom.db")
>>>

Initialise the model.

class mendevi.models.dnn.DNN_EncodeEnergy(*args: tuple, **kwargs: dict)

Deep neuronal network for the video encoding energy prediction.

Examples

>>> from mendevi.models.dnn import DNN_EncodeEnergy
>>> model = DNN_EncodeEnergy().fit("svtav1_vs_rav1e_vs_aom.db", table="t_enc_encode")
>>> model.validate("svtav1_vs_rav1e_vs_aom.db")
>>>

Initialise the model.

class mendevi.models.dnn.DNN_Quality(*args: tuple, **kwargs: dict)

Deep neuronal network for the video quality prediction.

Examples

>>> from mendevi.models.dnn import DNN_Quality
>>> model = DNN_Quality(output_labels=["psnr", "log_rev_ssim"])
>>> model.fit("svtav1_vs_rav1e_vs_aom.db", select="uniform(vid_hash) >= 0.2")
>>> model.validate("svtav1_vs_rav1e_vs_aom.db", select="uniform(vid_hash) < 0.2")
>>>

Initialise the model.