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.

/pdf/qoe-modeling-for-http-adaptive-video-streaming-a-survey-and-4ugy26rj1k.pdf

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

Computed tomography (CT) plays an essential role in clinical diagnosis. Sparse sampling is an effective way to reduce the radiation on patients, but it will lead to severe artifacts on the reconstructed CT images, and artifacts may interfere with the diagnosis. Therefore, it is significant to ensure the quality of the region of interest, with extra parameters as few as possible. In this paper, we design a novel lightweight network called CAGAN and propose a dual-domain reconstruction pipeline for parallel beam sparse-view CT. CAGAN is an adversarial auto-encoder with the coordinate attention (CA), which preserves the spatial information of features and learns to focus on the textures. Also, the application of lightweight convolutional blocks reduces the parameters by a quarter without performance loss. Taking 180 views as full-view, our model reaches a PSNR of 41.975 given 45 views and a PSNR of 34.546 given 10 views, which is extremely sparse.

A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction

In 5G networks, optimization of antenna beam weights of base stations has become the key application of AI for network optimization. For 6G, higher frequency bands and much denser cells are expected, and the importance of automatic and accurate beamforming assisted by AI will become more prominent. In existing network, servers are "patched" to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead, large inference latency and potential risks of data security. Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response, but it is inefficient in collecting and sharing global information among base stations. In this paper, we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization. We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation. Compared with the centralized solution, our solution improves prediction accuracy by 24.66%, and reduces storage cost by 83.82%. Compared with the decentralized solution, our solution improves prediction accuracy by 68.26%, and improves coverage performance by 0.4dB. At last, the future research work is prospected.

Cloud-assisted distributed edge brains for multi-cell joint beamforming optimization for 6G

A lot of study has been done on the voice QoE (Quality of Experience). However, QoE study on the background of Chinese language is scarce. PESQ (Perceptual evaluation of speech quality) is a well known objective method for the voice QoE evaluation, it is approved as ITU-T P.862 Recommendations. This paper research the accuracy of PESQ in evaluating speech codec in Chinese environment. Two 3G speech codecs are chosen, including Adaptive Multi-Rate codec (AMR) and Enhanced Variable Rate Codec (EVRC), which all used in 3G communication systems. In this paper, we firstly construct a Chinese speech database, which is used to the study of voice QoE. Then, do lots of experimental test, to get the accuracy of PESQ for evaluating the two codecs under Chinese database. And analyze the reason of the experiment result. Finally, in order to improve the accuracy of PESQ for speech codec, we proposed the improved of the PESQ algorithm.

Study the Voice QoE for Speech Codec in Chinese Environment

Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations. However, its severe imaging noise and streaking artifacts turn out to be a major issue in the low dose protocol. In this paper, we propose a dual-domain deep learning-based method that breaks through the limitations of currently prevailing algorithms that merely process single image slices. Since the scanned object usually contains a high degree of spatial continuity, the obtained consecutive imaging slices embody rich information that is largely unexplored. Therefore, we establish a cascade model named LS-AAE which aims to tackle the above problem. In addition, in order to adapt to the social trend of lightweight medical care, our model adopts the inverted residual with linear bottleneck in the module design to make it mobile and lightweight (reduce model parameters to one-eighth of its original) without sacrificing its performance. In our experiments, sparse sampling is conducted at intervals of 4°, 8° and 16°, which appears to be a challenging sparsity that few scholars have attempted before. Nevertheless, our method still exhibits its robustness and achieves the state-of-the-art performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while ensuring high model mobility. Particularly, it still exceeds other current methods when the sampling rate is one-fourth of them, thereby demonstrating its remarkable superiority.

A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction.

Virtual environments (VEs) have been widely used in multimedia applications, with trends toward larger data size. 3D streaming has recently been proposed to enable users to view the VEs in real time. However, how to efficiently download the VEs over a bandwidth-limited network becomes a bottleneck. With the aim of minimizing the scene distortion and achieving a more efficient use of bandwidth, it’s necessary to download the visible 3D contents at an appropriate resolution. In this paper, we propose a distortion-aware VEs transmission scheme. The main contributions of the proposed scheme include: (1) Improving the traditional interest management algorithm, so that our method can fit the user’s visual and behavioral characteristics better. (2) Proposing a scene distortion estimation algorithm, by which we can effectively calculate and evaluate the scene distortion. (3) Based on (1) and (2), we propose a scene update strategy, which combines the user’s visual characteristics, scene distortion and bandwidth estimation. The experimental results show that the proposed scheme can lower distortion especially when network condition is poor.

Yitong Liu

Papers

A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction

Cloud-assisted distributed edge brains for multi-cell joint beamforming optimization for 6G

Study the Voice QoE for Speech Codec in Chinese Environment

A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction.

Distortion-aware Virtual Environments Transmission Scheme over a Bandwidth-limited Network