Quantifying perceptual quality model uncertainty via bootstrapping
US12075104B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 13, 2019 |
| Grant date | Aug 27, 2024 |
| Priority date | — |
| Expiry date | Mar 13, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/147
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
In various embodiments, a bootstrapping training subsystem performs sampling operation(s) on a training database that includes subjective scores to generate resampled dataset. For each resampled dataset, the bootstrapping training subsystem performs machine learning operation(s) to generate a different bootstrap perceptual quality model. The bootstrapping training subsystem then uses the bootstrap perceptual quality models to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for a portion of encoded video content. Advantageously, relative to prior art solutions in which the accuracy of a perceptual quality score is unknown, the bootstrap perceptual quality models enable developers and software applications to draw more valid conclusions and/or more reliably optimize encoding operations based on the perceptual quality score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.