Training a Deep Neural Network (DNN) to predict an individual's opinion score regarding the quality of multimedia content is a recent research direction. This type of DNN is called Artificial Intelligence-based Observer (AIO). By generating individual opinion scores, AIOs enable the prediction of the Opinion Score Distribution (OSD) for a given multimedia content. Multimedia image quality assessment literature lacks contributions that thoroughly assess the ability of AIOs to predict the OSD. In this paper a new set of AIOs is trained and shown to predict the OSD more accurately than state-of-the-art methods.

Opinion Score Distribution Prediction via AI-Based Observers in Media Quality Assessment / Tiotsop, Lohic Fotio; Servetti, Antonio; Masala, Enrico. - STAMPA. - (2024), pp. 1-6. (Intervento presentato al convegno 18th IEEE International Conference on Application of Information and Communication Technologies, AICT 2024 tenutosi a Turin (ITA) nel 25-27 September 2024) [10.1109/aict61888.2024.10740409].

Opinion Score Distribution Prediction via AI-Based Observers in Media Quality Assessment

Tiotsop, Lohic Fotio;Servetti, Antonio;Masala, Enrico
2024

Abstract

Training a Deep Neural Network (DNN) to predict an individual's opinion score regarding the quality of multimedia content is a recent research direction. This type of DNN is called Artificial Intelligence-based Observer (AIO). By generating individual opinion scores, AIOs enable the prediction of the Opinion Score Distribution (OSD) for a given multimedia content. Multimedia image quality assessment literature lacks contributions that thoroughly assess the ability of AIOs to predict the OSD. In this paper a new set of AIOs is trained and shown to predict the OSD more accurately than state-of-the-art methods.
2024
979-8-3503-8753-7
File in questo prodotto:
File Dimensione Formato  
IEEE_official_Opinion_Score_Distribution_Prediction_via_AI-Based_Observers_in_Media_Quality_Assessment.pdf

accesso riservato

Descrizione: editorial version
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.18 MB
Formato Adobe PDF
1.18 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Paper_AICT_2024.pdf

accesso aperto

Descrizione: author version
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 1.13 MB
Formato Adobe PDF
1.13 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2998626