Multivariate rate control for transcoding video content
US11924449B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2020 |
| Grant date | Mar 5, 2024 |
| Priority date | — |
| Expiry date | May 19, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/196
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.