Quantifying perceptual quality model uncertainty via bootstrapping

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

• 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;
• and
• computing a plurality of bootstrap perceptual quality scores based on the bootstrap perceptual models and a portion of encoded video content to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for the portion of encoded video content.

A system, comprising:

• one or more memories storing instructions;
• and
• one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
• perform one or more resampling with replacement operations on a training database that includes subjective scores and feature values to generate a plurality of resampled datasets;
• for each resampled dataset, perform one or more machine learning operations based on the resampled dataset to generate a different bootstrap perceptual quality model;
• and
• perform one or more operations based on the bootstrap perceptual models to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for a portion of encoded video content.