Google llc (20240137400). METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION simplified abstract
Contents
- 1 METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION
Organization Name
Inventor(s)
Ching Yin Derek Pang of San Jose CA (US)
Kyrah Felder of Kennesaw GA (US)
Akshay Gadde of Fremont CA (US)
Paul Wilkins of Cambridge (GB)
Cheng Chen of Milpitas CA (US)
Yao-Chung Lin of Sunnyvale CA (US)
METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240137400 titled 'METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION
Simplified Explanation
The abstract describes a patent application related to using machine learning to predict encoder parameter settings for encoding media items based on historical data.
- Machine learning model trained on historical encoding data
- Predicts encoder parameter settings for a given media item
- Historical data includes prior encoder parameter settings for similar media items
- Encoder parameter settings are determined based on model output
- Media item is encoded using the determined settings
Potential Applications
This technology could be applied in various industries such as video streaming services, online gaming platforms, and social media platforms to optimize media encoding processes.
Problems Solved
This technology solves the problem of manually determining encoder parameter settings for each media item, saving time and improving efficiency in the encoding process.
Benefits
The benefits of this technology include improved encoding quality, faster encoding times, and overall cost savings for companies that deal with large amounts of media content.
Potential Commercial Applications
"Optimizing Media Encoding with Machine Learning" could be a suitable title for this section. This technology could be commercially applied in video streaming services, online gaming platforms, and social media platforms to enhance user experience and reduce operational costs.
Possible Prior Art
There may be prior art related to using machine learning for optimizing media encoding processes, but specific examples are not provided in this abstract.
Unanswered Questions
How does this technology handle different types of media formats and codecs?
This article does not address how the machine learning model adapts to various media formats and codecs to predict encoder parameter settings accurately.
What kind of performance criteria are used to determine the encoder parameter settings?
The abstract does not specify the performance criteria used to evaluate the encoder parameter settings predicted by the machine learning model.
Original Abstract Submitted
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.
