Julie D Novak

US Patent:

20190295242, Sep 26, 2019

Filed:

Mar 13, 2019

Appl. No.:

16/352757

Inventors:

- Los Gatos CA, US
Zhi LI - Mountain View CA, US
Lavanya SHARAN - Menlo Park CA, US
Julie NOVAK - San Jose CA, US
Martin TINGLEY - Los Gatos CA, US

International Classification:

G06T 7/00
H04N 19/154

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.

US Patent:

20190297329, Sep 26, 2019

Filed:

Mar 13, 2019

Appl. No.:

16/352755

Inventors:

- Los Gatos CA, US
Zhi LI - Mountain View CA, US
Lavanya SHARAN - Menlo Park CA, US
Julie NOVAK - San Jose CA, US
Martin TINGLEY - Los Gatos CA, US

International Classification:

H04N 19/154
G06T 7/00

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.

Author:

Julie C. Novak

ISBN #:

0323003222

Author:

Julie C. Novak

ISBN #:

0323003257

Author:

Julie C. Novak

ISBN #:

0815164483