Abstract: A media item to be provided to users of a platform is identified. The media item is associated with a media class of one or more media classes. An indication of the media item is provided as input to a machine learning model trained based on historical encoding data to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class of the given media item. The historical encoding data includes a prior set of encoder parameter settings that satisfied the performance criterion with respect to a prior media item associated with the respective class. Encoder parameter settings that satisfy the performance criterion in view of the media class is determined based on an output of the model. The media item is caused to be encoded using the determined encoder parameter settings.

Abstract: Methods and systems for encoder parameter setting optimization. A media item to be provided to one or more users of a platform is identified. The media item is associated with a media class. An indication of the identified media item is provided as input to a first machine learning model. The first machine learning model is trained to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item. One or more outputs of the first machine learning model are obtained. The one or more obtained outputs include encoder data identifying one or more sets of encoder parameter settings and, for each of the sets of encoder parameter settings, an indication of a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item.

Abstract: Methods and systems for encoder parameter setting optimization. A media item to be provided to one or more users of a platform is identified. The media item is associated with a media class. An indication of the identified media item is provided as input to a first machine learning model. The first machine learning model is trained to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item. One or more outputs of the first machine learning model are obtained. The one or more obtained outputs include encoder data identifying one or more sets of encoder parameter settings and, for each of the sets of encoder parameter settings, an indication of a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item.