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

High quality transmission of Virtual Reality (VR) video depends strictly on bandwidth and delay requirements. It becomes possible with the maturity and commercialization of 5G technology. However, to transmit VR video streaming over wireless communication system, we must cope with the challenge that fluctuation exists in the wireless channel conditions. For example, available channel bandwidth, packet loss rate, and interference level may vary with time due to channel fading and user's mobility. The problem is more prominent when transmitting naked-eye 3D video which generally consists of multiple viewpoints with different resolutions. In order to optimize the quality of experience (QoE) of watching naked-eye VR video over wireless networks, this paper proposes a network-assisted neural adaptive video streaming algorithm (NAVSA). Specifically, we present a modified QoE function to describe the quality of naked-eye 3D streaming quantitatively, which not only considers the quality of naked-eye 3D video itself, but also considers the phenomenon of rebuffering and video fluctuation that occurs during video transmission. Next, with the network-assisted feedback, the physical layer information, the buffer occupancy of the video client, and the size of the next video chunk are collected to train a reinforcement learning model. Based on dynamic adaptive streaming over HTTP (DASH), the model can automatically choose appropriate viewpoints and resolutions corresponding to the current condition of the wireless networks such that the network capacity can be fully explored. To verify the performance of our proposed NAVSA, we simulate 3 naked-eye 3D video application scenarios on the NS3 platform. The results show that the performance of NAVSA is about 5~8% better than some state-of-the-art algorithms in wireless networks.

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Network-Assisted Neural Adaptive Naked-Eye 3D Video Streaming Over Wireless Networks

The performance of Perceptual Evaluation of Speech Quality (PESQ), a most prevailing voice service Quality of Experience (QoE) assessment method, is evaluated in Chinese environment. A normative Chinese speech database has been developed, especially considered the distortions from the real transmission situation. Through the huge subjective result, a distinct conclusion can be drawn that it is not convinced to use PESQ assess Chinese voice service directly. A non-linear compensation factor is proposed in this paper to make the universal PESQ be suitable for the Chinese voice service accurately. After compensation, the score of PESQ matches the Mean Opinion Score (MOS) much more, and the residual error distribution has also been improved greatly compared with the result given in ITU P.862.1.

Performance Evaluation and Accuracy Upgrading of PESQ in Chinese Environment

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. key words: HEVC, fast algorithm, early termination, AdaBoost

Fast CU Termination Algorithm with AdaBoost Classifier in HEVC Encoder

The invention relates to a general technique of applying a watermark to a radio station. The principle of the general technique is shown in Figure 1. At a transmitting end, a separate device is inserted into the output end of a local oscillator, the device is used for additionally modulating an oscillation source by using a PN (Pseudo-Noise) sequence for only identifying the station, so as to insert the watermark into carrier waves output by the oscillator, and a signal which needs to be sent by the station is transmitted on the basis of the carrier waves into which the watermark is inserted, i.e. an ID (Identity) which identifies the station is added. At a receiving end, a separate and simple device can be used for identifying and extracting the watermark information which is embedded by the oscillator of the station. On the premise that transmitted contents are not processed, the problem of applying the watermark to the radio station is solved by using the method, and normal transmitting and receiving of the signal are not affected.

Method for applying watermark to radio station

Along with the rapid development of 3G and 4G technologies, mobile video services have gained its popularity among users around the world. Consequently, Content Providers (CPs), Service Providers (SPs), and especially, the operators are paying increasing attentions to the quality of experience (QoE) of the video services which could be easily affected by the quality of network. In this paper, a novel real-time objective video QoE assessment method is proposed and a software assessment system is built to test the video service quality in the real network. Firstly, in the test terminals, the QoE measurement of the entire video services is conducted by collecting all of the customers’ experience in full-reference method, and then the QoE scores are evaluated through an accurate mathematic model. Secondly, the artifact of compression caused by video encoding should also be taken into account. Model in this part adopts no-reference method in consideration of the varied screen sizes in different terminals. What’s more, the platform also evaluates the error of network in the part of video transmission by associating no-reference PSNR with network delay, jitter, and packet loss ratio. The results of Mean Opinion Score (MOS) tests show that the proposed models estimate QoE with high quality estimation accuracy respectively. We develop a software toolkit using the test methodologies above, which can help the operators to make measurements for its network. This software toolkit is useful as a QoE monitoring tool on video streaming services and can be deployed on real network conveniently

Yitong Liu

Papers

Network-Assisted Neural Adaptive Naked-Eye 3D Video Streaming Over Wireless Networks

Performance Evaluation and Accuracy Upgrading of PESQ in Chinese Environment

Fast CU Termination Algorithm with AdaBoost Classifier in HEVC Encoder

Method for applying watermark to radio station

A Software Platform Design for Objective Video QoE Assessment