CPC H04N 21/252 (2013.01) [G06F 18/214 (2023.01); G06N 20/20 (2019.01); G06T 7/0002 (2013.01); G06V 10/774 (2022.01); H04N 19/154 (2014.11); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30168 (2013.01); H04N 17/004 (2013.01); H04N 19/147 (2014.11)] | 9 Claims |
1. A computer-implemented method, comprising:
performing one or more sampling operations on a training database that includes subjective scores to generate a plurality of resampled datasets;
for each resampled dataset, performing one or more machine learning operations based on the resampled dataset to generate a different bootstrap perceptual quality model;
generating a plurality of perceptual quality scores for a portion of encoded video content using the different bootstrap perceptual quality models; and
performing one or more operations based on the bootstrap perceptual quality models to generate a first confidence interval from a subset of the plurality of perceptual quality scores that meets a first threshold criterion, wherein the first confidence interval quantifies an quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for the portion of encoded video content, wherein the baseline perceptual quality model does not comprise one of the bootstrap perceptual quality models, and wherein the portion of encoded video content is not included in the training database.
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