QoE Prediction for Enriched Assessment of Individual Video Viewing Experience

Most automatic Quality of Experience (QoE) assessment models have so far aimed at predicting the QoE of a video as experienced by an average user, and solely based on perceptual characteristics of the video being viewed. The importance of other characteristics, such as those related to the video content being watched, or those related to an individual user have been largely neglected. This is suboptimal in view of the fact that video viewing experience is individual and multifaceted, considering the perceived quality (related to coding or network-induced artifacts), but also other -- more hedonic - aspects, like enjoyment. In this paper, we propose an expanded model which aims to assess QoE of a given video, not only in terms of perceived quality but also of enjoyment, as experienced by a specific user. To do so, we feed the model not only with information extracted from the video (related to both perceived quality and content), but also with individual user characteristics, such as interest, personality and gender. We assess our expanded QoE model based on two publicly available QoE datasets, namely i_QoE and CP-QAE-I. The results show that combining various types of characteristics enables better QoE prediction performance as compared to only considering perceptual characteristics of the video, both when targeting perceived quality and enjoyment.