18403659. METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION simplified abstract (GOOGLE LLC)
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 18403659 titled 'METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION
Simplified Explanation
The abstract describes a method for predicting encoder parameter settings for encoding media items based on historical encoding data and a machine learning model.
- The method involves identifying a media item associated with a media class and providing an indication of the media item as input to a machine learning model.
- The model is trained based on historical encoding data to predict encoder parameter settings that satisfy a performance criterion for the given media item and its respective media class.
- Encoder parameter settings are determined based on the model output, and the media item is encoded using these settings.
Potential Applications
This technology could be applied in the fields of video streaming services, online content platforms, and digital media production.
Problems Solved
This technology helps optimize the encoding process for different types of media items, ensuring better performance and quality output.
Benefits
The benefits of this technology include improved efficiency in media encoding, enhanced user experience with high-quality media content, and potentially reduced encoding costs.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of encoding software for media companies and streaming platforms.
Possible Prior Art
Prior art in this field may include research on machine learning models for optimizing encoding parameters, as well as studies on media class-based encoding techniques.
What are the limitations of this technology in real-world applications?
One limitation of this technology in real-world applications could be the need for a large amount of historical encoding data to train the machine learning model effectively.
How does this technology compare to traditional methods of media encoding?
This technology offers a more automated and optimized approach to media encoding compared to traditional manual methods, potentially leading to better performance and efficiency.
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.
