부산대학교 도서관

With the explosion of multimedia data, quality of experience (QoE) has become a critical metric in multimedia transmission, and therefore, QoE-oriented image quality assess-ment (IQA) turns more important and urgent. However, the performance of the traditional user-based assessment methods is limited by the deviation caused by human cognitive activities. In this paper, we propose a brain-inspired IQA method based on electroencephalography (EEG) feature learning, which is a psychophysiological method for studying human perception for IQA. We first establish the EEG dataset by collecting the corresponding EEG signals when subjects watch distorted facial images and then design a siamese network to extract the EEG features that can distinguish image quality levels and measure user scores. The siamese network establishes the relationship between image quality and QoE that is reflected by the EEG scores. The relationship is then embedded into a prediction network that directly obtains the EEG scores from images with different qualities. In this way, EEG scores can be predicted through end-to-end learning. Experiment results show that our proposed method can not only better evaluate the perceptual quality of facial images and reflect real human perceptions but also achieve better score prediction performance on the facial image datasets.