Video Quality Evaluation for Tile-Based Spatial Adaptation

The demand for very high-resolution video content in entertainment services (4K, 8K, panoramic, 360 VR) puts an increasing load on the distribution network. In order to reduce the network usage in existing delivery infrastructure for such services while keeping a good quality of experience, dynamic spatial video adaptation at the client side is seen as a key feature, and is actively investigated by academics and industrials. However, the impact of spatial adaptation on quality perception is not clear. In this paper, we propose a methodology for the evaluation of such adapted content, conduct a series of perceived quality measurements and discuss results showing potential benefits and drawbacks of the technique. Based on our results, we also propose a signaling mechanism in MPEGDASH to assist the client in its spatial adaptation log

VIDEO PREPROCESSING BASED ON HUMAN PERCEPTION FOR TELESURGERY

Video transmission plays a critical role in robotic telesurgery because of the high bandwidth and high quality requirement. The goal of this dissertation is to find a preprocessing method based on human visual perception for telesurgical video, so that when preprocessed image sequences are passed to the video encoder, the bandwidth can be reallocated from non-essential surrounding regions to the region of interest, ensuring excellent image quality of critical regions (e.g. surgical region). It can also be considered as a quality control scheme that will gracefully degrade the video quality in the presence of network congestion. The proposed preprocessing method can be separated into two major parts. First, we propose a time-varying attention map whose value is highest at the gazing point and falls off progressively towards the periphery. Second, we propose adaptive spatial filtering and the parameters of which are adjusted according to the attention map. By adding visual adaptation to the spatial filtering, telesurgical video data can be compressed efficiently because of the high degree of visual redundancy removal by our algorithm. Our experimental results have shown that with the proposed preprocessing method, over half of the bandwidth can be reduced while there is no significant visual effect for the observer. We have also developed an optimal parameter selecting algorithm, so that when the network bandwidth is limited, the overall visual distortion after preprocessing is minimized

2004 IEEE International Conference on Multimedia and Expo (ICME) A Content-based Bit Allocation Model for Video Streaming

Traditional video coding and streaming algorithms do not been fully utilized in content information of video in bit allocation and adaptation. This paper proposes a content-based video streaming method based on visual attention model to better utilize network bandwidth and achieve better subjective video quality. First, visual attention model is exploited to segment the Regions of Interest (ROI) in video frames. Then, considering the ROI is more sensitive to coding error than other regions, a region-weighted rate-distortion model is developed to allocate suitable bits for all ROI and non-ROI regions. The evaluation results indicate that more than 60 % of the test video sequences encoded by the proposed method can obtain better subjective visual quality compared to the video encoded by classical method under the same bandwidth, and about 20 % of them can not be distinguished