다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The high definition (HD) and ultra HD videos can be widely applied in broadcasting applications. However, with the increased resolution of video, the volume of the raw HD visual information data increases significantly, which becomes a challenge for storage, processing, and transmitting the HD visual data. The state-of-the-art video compression standard-H.265/High Efficiency Video Coding (HEVC) compresses the raw HD visual data efficiently, while the high compression rate comes at the cost of heavy computation load. Hence, reducing the encoding complexity becomes vital for the H.265/HEVC encoder to be used in broadcasting applications. In this paper, based on the best motion vector selection correlation among the different size prediction modes, we propose a fast motion estimation (ME) method to reduce the encoding complexity of the H.265/HEVC encoder. First, according to the prediction unit (PU) partition type, all PUs are classified into two classes, parent PU and children PUs, respectively. Then, based on the best motion vector selection correlation between the parent PU and children PUs, the block matching search process of the children PUs is adaptively skipped if their parent PU chooses the initial search point as its final optimal motion vector in the ME process. Experimental results show that the proposed method achieves an average of 20% ME time saving as compared with the original HM-TZSearch. Meanwhile, the rate distortion performance degradation is negligible.

Fast Motion Estimation Based on Content Property for Low-Complexity H.265/HEVC Encoder

With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.

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QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges

Over the last few years, the evolution of network and user handsets’ technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and added-value services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Most of the methods in operators' current 5G networks use expert knowledge assisted by machine learning algorithms to generate optimization decisions. However, these methods are inadaptive to the dynamic changes of high-dimensional network states, thus the result is often suboptimal. Reinforcement learning can better cope with high-dimensional network state space and parameter space. However, when applied to real network, challenges arise such as difficult to obtain data samples, time-consuming and risky to explore real networks during model training. To solve these problems, this paper proposes a combined approach of expert knowledge, reinforcement learning and digital twin for the self-optimization of mobile networks. By constructing a digital twin of the current network, the future network state is predicted based on which optimization decisions are generated by expert knowledge and reinforcement learning respectively, and then input into the digital twin. Digital twin simulates their rewards and decides a final action for execution. Simulation results have confirmed that the proposed scheme can achieve higher rewards than either expert knowledge or reinforcement learning, and can avoid negative impact on real network performance. This paper also describes several potential application scenarios for the proposed approach in 6G networks and discusses key issues for future research.

A Digital Twin Approach for Self-optimization of Mobile Networks

In this paper, we propose a Quality of Experience (QoE) model for adaptive streaming services based on bitrate distribution to evaluate customers' subjective perception accurately Unlike the QoE assessment for constant bitrate (CBR) video services, different bitrate distribution causes significant difference for the QoE of adaptive streaming Therefore, we design a method based on the Primacy Effect and Recency Effect, a psychological phenomenon that the initial and recent information are more prominent in short-term memory for people, to analyze and quantify the QoE influence caused by bitrate distribution Besides the bitrate distribution, real-time bitrate and video content types are also mapped into our QoE model to provide great QoE evaluation for adaptive streaming We evaluate our QoE model via plenty of subjective Mean Opinion Score (MOS) tests, which include 16 test samples with 659 votes The Pearson Correlation Coefficient between our QoE score and MOS achieves to 097, which indicates that our model can evaluate customers' perception on adaptive streaming quality accurately Besides, we compare our QoE model to the average QoE evaluation method Simulation results show that our QoE model is more flexible and powerful to reflect customers' feeling on adaptive streaming services with various kinds of bitrate distributions

A method of QoE evaluation for adaptive streaming based on bitrate distribution

The growing demand on video streaming services is the main motive power for video quality assessment. Due to the rapid development of video streaming services, customers are more enjoying higher quality videos, and their perceptions on video service will directly influence the service provider's performance, which makes it significant to study end-users' subjective perception, named as Quality of Experience (QoE), on video streaming for both service providers and end-users. In this paper, a QoE evaluation model is proposed to predict the end-users' perception on video streaming service considering different video content types. This QoE model, named as Video-Mean Opinion Score (VMOS) model, directly focuses on end-users' feeling. Thus, the Key Performance Indicators (KPIs), which can be straightly felt by end-users, are mapped to QoE score without considering the network parameters. The excellent performance of VMOS model for QoE evaluation on video quality has been verified via plenty subjective Mean Opinion Score (MOS) test, which includes 180 video samples with 1280 valid votes. The Pearson Correlation Coefficient between VMOS score and MOS is as high as 0.925, which indicates that this model can evaluate users' perception on video quality with almost the same accuracy as subjective test.

QoE-based evaluation model on video streaming service quality

Quality of Experience Study on Dynamic Adaptive Streaming Based on HTTP

Nowadays, adaptive streaming is quite popular and is expected to develop at a high rate of speed Dynamic Adaptive Streaming over HTTP (DASH) is a new 3GPP and MPEG standard for adaptive steaming over HTTP which is currently being extensively accepted in the industry DASH requests best quality of media under available bandwidth with adaptive algorithm which is implemented in clients To obtain the appropriate quality of media, an accurate estimate of the bandwidth is needed In this paper, we present a novel algorithm to estimate the bandwidth even more accurately This bandwidth estimation algorithm involves bandwidth detective method and bandwidth computing method Outliers within a short term, slight fluctuations and persistent variations can be distinguished through our proposed bandwidth detective method Hereafter, we compute the estimated bandwidth in different methods based on the result of bandwidth detection We build a DASH platform and compare our proposed algorithm to the VLC adaptive algorithm According to the results, it is demonstrated that our algorithm possesses more accurate bandwidth estimation and higher buffer level Our proposed algorithm is sensitive to persistent variations and robust to slight fluctuations

Yitong Liu

Papers

A Digital Twin Approach for Self-optimization of Mobile Networks

A method of QoE evaluation for adaptive streaming based on bitrate distribution

QoE-based evaluation model on video streaming service quality

Quality of Experience Study on Dynamic Adaptive Streaming Based on HTTP

Bandwidth estimation of rate adaption algorithm in DASH