- Patent Title: Quantifying perceptual quality model uncertainty via bootstrapping
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Application No.: US16352755Application Date: 2019-03-13
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Publication No.: US12075104B2Publication Date: 2024-08-27
- Inventor: Christos Bampis , Zhi Li , Lavanya Sharan , Julie Novak , Martin Tingley
- Applicant: NETFLIX, INC.
- Applicant Address: US CA Los Gatos
- Assignee: NETFLIX, INC.
- Current Assignee: NETFLIX, INC.
- Current Assignee Address: US CA Los Gatos
- Agency: Artegis Law Group, LLP
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06F18/214 ; G06N20/20 ; G06V10/774 ; H04N19/154 ; H04N21/25 ; H04N17/00 ; H04N19/147

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.
Public/Granted literature
- US20190297329A1 QUANTIFYING PERCEPTUAL QUALITY MODEL UNCERTAINTY VIA BOOTSTRAPPING Public/Granted day:2019-09-26
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