Google llc (20240187618). MULTIVARIATE RATE CONTROL FOR TRANSCODING VIDEO CONTENT simplified abstract
Contents
- 1 MULTIVARIATE RATE CONTROL FOR TRANSCODING VIDEO CONTENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTIVARIATE RATE CONTROL FOR TRANSCODING VIDEO CONTENT - 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
MULTIVARIATE RATE CONTROL FOR TRANSCODING VIDEO CONTENT
Organization Name
Inventor(s)
Balineedu Adsumilli of Sunnyvale CA (US)
Akshay Gadde of Fremont CA (US)
MULTIVARIATE RATE CONTROL FOR TRANSCODING VIDEO CONTENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240187618 titled 'MULTIVARIATE RATE CONTROL FOR TRANSCODING VIDEO CONTENT
Simplified Explanation
The learning model in this patent application is trained to predict rate-distortion behavior for video data on a video hosting platform, allowing for optimal bitrate allocations based on video content complexity.
- The learning model processes complexity features of video data to predict rate-distortion clusters, which are used to select transcoding parameters for the video data.
- Rate-distortion clusters are modeled during training based on rate-distortion curves and classifications of video data in the platform's corpus.
- This approach minimizes total corpus egress and storage while maintaining uniform video quality delivery.
Potential Applications
This technology could be applied in video streaming services, online video platforms, and video transcoding systems.
Problems Solved
This technology solves the problem of inefficient bitrate allocation and transcoding processes for video data with varying complexity levels.
Benefits
The benefits of this technology include optimized bitrate allocations, reduced storage and egress costs, and consistent video quality delivery.
Potential Commercial Applications
Potential commercial applications include video hosting platforms, content delivery networks, and video streaming services.
Possible Prior Art
One possible prior art for this technology could be research on video transcoding optimization and rate-distortion modeling in video processing systems.
== What are the limitations of the learning model in predicting rate-distortion behavior for video data? The limitations of the learning model in predicting rate-distortion behavior for video data may include the accuracy of complexity feature processing, the generalization of rate-distortion clusters across different video content, and the scalability of the model to large video hosting platforms.
== How does this technology compare to traditional bitrate allocation methods in video transcoding? This technology differs from traditional bitrate allocation methods by using a learning model to predict rate-distortion behavior based on video content complexity, allowing for more efficient transcoding processes and optimized video quality delivery.
Original Abstract Submitted
a learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. the rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. this approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.
