US 12,273,521 B2
Obtaining video quality scores from inconsistent training quality scores
Yilin Wang, Sunnyvale, CA (US); and Balineedu Adsumilli, Sunnyvale, CA (US)
Assigned to GOOGLE LLC, Mountain View, CA (US)
Filed by GOOGLE LLC, Mountain View, CA (US)
Filed on Jul. 12, 2022, as Appl. No. 17/862,571.
Prior Publication US 2024/0022726 A1, Jan. 18, 2024
Int. Cl. H04N 19/13 (2014.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01)
CPC H04N 19/13 (2014.11) [G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06T 7/0002 (2013.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving a training dataset,
wherein the training dataset comprises a first dataset and a second dataset,
wherein the first dataset comprises a first subset of first videos corresponding to a first context and respective first ground truth quality scores of the first videos, and
wherein the second dataset comprises a second subset of second videos corresponding to a second context that is different from the first context and respective second ground truth quality scores of the second videos; and
training a machine learning model to predict the respective first ground truth quality scores and the respective second ground truth quality scores, wherein training the machine learning model comprises:
training the machine learning model to obtain a global quality score for one of the videos of the training dataset, wherein the global quality score is context independent; and
training the machine learning model to map the global quality score to context-dependent predicted quality scores, wherein the context-dependent predicted quality scores comprising a first context-dependent predicted quality score corresponding to the first context and a second context-dependent predicted quality score corresponding to the second context.