6 accepted papers
Effective modelling of uncertain information is crucial for quantifying uncertainty. Dempster–Shafer evidence (DSE) theory is a widely recognized approach for handling uncertain information. However, current methods often neglect the inherent a priori information within data during modelling, and im…
Electroencephalogram (EEG) signal processing has advanced in revealing the mechanisms of human visual perception, but existing methods often overlook two key EEG properties: (1) 3D geometric relationships between EEG electrodes, which reflects the ability to model the brain in stereoscopic terms; an…
Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios. In this paper, we propose an FR-IQA method based on abdu…
Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes.…
This paper proposes a global optimization solution that combines different types of perceptual information to further improve transmission efficiency. First, a new rate-distortion (R-D) model is proposed to reflect the characteristics of 360degree video. Second, a globally optimized adaptive bitrate…
A joint reinforcement learning (RL) and game theory method is presented for segment-level continuous bitrate selection and tile-level bitrate allocation in tile-based 360-degree streaming to increase users’ quality of experience (QoE). First, a viewpoint prediction method based on single-user (SU) v…