In this paper, we design an Unequal Error Protection (UEP) rateless code with special coding graph and apply it to propose a novel HTTP adaptive streaming based on UEP rateless code (HASUR). Our designed UEP rateless code provides high diversity on decoding probability and priority for data in different important level with overhead smaller than 0.27. By adopting this UEP rateless channel coding and scalable video source coding, our HASUR ensures symbols with basic quality to be decoded first to guarantee fluent playback experience. Besides, it also provides multiple layers to ensure the most suitable quality for fluctuant bandwidth and packet loss rate (PLR) without estimating them in advance. We evaluate our HASUR against the alternative solutions. Simulation results show that HASUR provides higher video quality and more adapts to bandwidth and PLR than other two commercial schemes under End-to-End transmission.

In this paper, the Quality of Experience (QoE) on Dynamic Adaptive Streaming based on HTTP (DASH) is researched. To study users' experience on DASH, extensive subjective tests are firstly designed and conducted, based on which, we research QoE enhancement in DASH and find that DASH ensures more fluent playback (less stall) than constant bitrate (CBR) streaming to promote users' satisfaction especially in mobile networks. Then we adopt two-way analysis of variance (ANOVA) tests in statistics to identify the effect of specific factors (segment bitrate, bitrate fluctuation pattern, and bitrate switching) that impair users' experience on DASH. The impairment functions are then derived for these influence factors based on the Primacy and Recency Effect, a psychological phenomenon that has been proved to exist in users' experience on DASH in this paper. And the final QoE evaluation model is proposed to provide high correlation assessment for QoE of DASH. The good performance of our QoE model is validated by the subjective tests. In addition, our QoE study on DASH is also applied for QoE management to propose a QoE-based bitrate adaptation strategy, which promotes users' experience on DASH more strongly than the strategy based on QoS.

High Efficiency Video Coding (HEVC) has a better coding efficiency comparing with H.264/AVC. However, performance enhancement results in increased computational complexity which is mainly brought by the quadtree based coding tree unit (CTU). In this paper, an early termination algorithm based on AdaBoost classifier for coding unit (CU) is proposed to accelerate the process of searching the best partition for CTU. Experiment results indicate that our method can save 39% computational complexity on average at the cost of increasing Bjontegaard-Delta rate (BD-rate) by 0.18.

Sparse-view CT reconstruction has gained significant attention due to the growing concerns about radiation safety. Although recent deep learning-based image domain reconstruction methods have achieved encouraging performance over iterative methods, effectively capturing intricate details and organ structures while suppressing noise remains challenging. This study presents a novel dual-stream encoder-decoder-based reconstruction network that combines global path reconstruction from the entire image with local path reconstruction from image patches. These two branches interact through an attention module, which enhances visual quality and preserves image details by learning correlations between image features and patch features. Visual and numerical results show that the proposed method has superior reconstruction capabilities to state-of-the-art 180-, 90-, and 45-view CT reconstruction methods.