Perceived media object quality prediction using adversarial annotations for training and multiple-algorithm scores as input

US11544562B2 · kind B2 · utility

Assignee

• AMAZON TECHNOLOGIES, INC. · US

Inventors

• Thomas Sydney Austin Wallis · Waltersdorf, DE
• Luitpold Staudigl · Bonn, DE
• Muhammad Bilal Javed · Berlin, DE
• Pablo Barbachano · Berlin, DE
• Mike Mueller · Phoenix, US

Key dates

• Filing date: May 15, 2020
• Grant date: Jan 3, 2023
• Expiry date: Jun 10, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/0985
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Respective labels indicative of compression-related quality degradation for a set of media object tuples which meet a divergence criterion are obtained; each tuple comprises a reference media object and a pair of corresponding compressed media object versions. Pairs of training records for a machine learning model are generated using the labeled media object tuples and multiple perceptual quality algorithms, with each training record comprising respective perceived quality degradation scores generated by each of the multiple algorithms for a given compressed media object of a tuple. A machine learning model is trained, using the record pairs, to predict quality degradation scores for compressed media objects.