US 12,417,522 B2
Method for constructing a perceptual metric for judging video quality
Troy Chinen, Fremont, CA (US); Alex Sukhanov, Sunnyvale, CA (US); Eirikur Thor Agustsson, Zurich (CH); and George Dan Toderici, Mountain View, CA (US)
Assigned to GOOGLE LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Sep. 27, 2021, as Appl. No. 17/486,359.
Prior Publication US 2023/0099526 A1, Mar. 30, 2023
Int. Cl. G06T 7/00 (2017.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); H04N 19/23 (2014.01)
CPC G06T 7/0002 (2013.01) [G06F 18/24 (2023.01); G06N 20/00 (2019.01); H04N 19/23 (2014.11); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30168 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A computer-implemented method for determining a perceptual quality of a subject video content item that is based on an encoding of a reference video content item, the method comprising:
generating, by one or more computing devices, subject flow data based at least in part on a frame sequence of the subject video content item;
generating, by the one or more computing devices, reference flow data based at least in part on a frame sequence of the reference video content item;
processing, by the one or more computing devices, the subject flow data and the reference flow data using a first machine-learned model to compare the subject flow data and the reference flow data, wherein processing the subject flow data and the reference flow data using the first machine-learned model comprises processing the subject flow data and the reference flow data using an adapted image classifier model;
generating, by the one or more computing devices and using the first machine-learned model, a temporal feature based at least in part on the subject flow data and the reference flow data; and
outputting, by the one or more computing devices using a second machine-learned model, a score indicating the perceptual quality of the subject video content item based at least in part on the temporal feature.