Linking Distortion Perception and Visual Saliency in H.264AVC Coded Video Containing Packet Loss In this paper, distortions caused by packet loss during video transmission are evaluated with respect to their perceived annoyance. In this respect, the impact of visual saliency on the level of annoyance is of particular interest, as regions and objects in a video frame are typically not of equal importance to the viewer. For this purpose, gaze patterns from a task free eye tracking experiment were utilised to identify salient regions in a number of videos. Packet loss was then introduced into the bit stream such as that the corresponding distortions appear either in a salient region or in a nonsalient region. A subjective experiment was then conducted in which human observers rated the annoyance of the distortions in the videos. The outcomes show a strong tendency that distortions in a salient region are indeed perceived as much more annoying as compared to distortions in the nonsalient region. The saliency of the distorted image content was further found to have a larger impact on the perceived annoyance as compared to the distortion duration. The findings of this work are considered to be of great use to improve prediction performance of video quality metrics in the context of transmission errors.

A QoEoriented Saliencyaware Approach for 360degree Video Transmission The tradeoff between bandwidth efficiency and quality of experience (QoE) is a key issue in 360 video transmission. In this paper, we propose a QoEoriented saliencyaware 360 video transmission framework to balance this tradeoff. The target is to reduce the bandwidth demand without declining the QoE. Specifically, the proposed model is based on the decisionmaking process. We use Lyapunov optimization to solve the decisionmaking problem. Furthermore, we integrate saliency information into the model to influence the decision policy, so that the model has the advantage of bandwidth efficiency. The simulation results show that the tradeoff parameter of Lyapunov optimization can balance the tradeoff between QoE and bandwidth efficiency, and 360 video saliency entropy limits the upper and lower bounds of QoE and bandwidth efficiency.