公开(公告)号：US11361416B2

公开(公告)日：2022-06-14

申请号：US16352757

申请日：2019-03-13

Applicant: NETFLIX, INC.

Inventor： Christos Bampis ,  Zhi Li ,  Lavanya Sharan ,  Julie Novak ,  Martin Tingley

IPC: H04N19/154 ,  G06T7/00 ,  G06K9/62 ,  G06N20/20 ,  H04N21/25 ,  H04N19/147 ,  H04N17/00

Abstract: In various embodiments, an encoding metric comparison application computes a first set of quality scores associated with a test encoding configuration based on a set of bootstrap quality models. Each bootstrap quality model is trained based on a different subset of a training database. The encoding metric comparison application computes a second set of quality scores associated with a reference encoding configuration based on the set of bootstrap quality models. Subsequently, the encoding metric comparison application generates a distribution of bootstrap values for an encoding comparison metric based on the first set of quality scores and the second set of quality scores. The distribution quantifies an accuracy of a baseline value for the encoding comparison metric generated by a baseline quality model.

公开(公告)号：US12075104B2

公开(公告)日：2024-08-27

申请号：US16352755

申请日：2019-03-13

Applicant: NETFLIX, INC.

Inventor： Christos Bampis ,  Zhi Li ,  Lavanya Sharan ,  Julie Novak ,  Martin Tingley

IPC: G06T7/00 ,  G06F18/214 ,  G06N20/20 ,  G06V10/774 ,  H04N19/154 ,  H04N21/25 ,  H04N17/00 ,  H04N19/147

CPC classification number: H04N21/252 ,  G06F18/214 ,  G06N20/20 ,  G06T7/0002 ,  G06V10/774 ,  H04N19/154 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/30168 ,  H04N17/004 ,  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.