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.File | Dimensione | Formato | |
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IEEE_official_Opinion_Score_Distribution_Prediction_via_AI-Based_Observers_in_Media_Quality_Assessment.pdf
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https://hdl.handle.net/11583/2998626