In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the q...In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.展开更多
Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from th...Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from the given sequence.Although such a learning scheme is generally effective,it has a critical limitation,i.e.,the model learned on sparse frames only possesses weak generalization ability.This situation could become worse on“long”videos since they tend to have intensive scene variations.Moreover,in such videos,the keyframe information from a longer time span is less relevant to the previous,which could also cause learning conflict and deteriorate the model performance.Thus,the learning scheme is usually incapable of handling complex pattern modeling.To solve this problem,we propose a divide-and-conquer framework,which can convert a complex problem domain into multiple simple ones.First,we devise a novel background consistency analysis(BCA)which effectively divides the mined frames into disjoint groups.Then for each group,we assign an individual deep model on it to capture its key attribute during the fine-tuning phase.During the testing phase,we design a model-matching strategy,which could dynamically select the best-matched model from those fine-tuned ones to handle the given testing frame.Comprehensive experiments show that our method can adapt severe background appearance variation coupling with object movement and obtain robust saliency detection compared with the previous scheme and the state-of-the-art methods.展开更多
弹幕视频是一种近年来发展迅速的新兴媒体传播形式,备受大众喜爱,但根据调查显示,部分用户认为弹幕严重影响了视频观看。为改善弹幕视频服务,用户体验质量(Quality of Experience,QoE)的评估迫在眉睫。然而,现有用户体验质量评价方法存...弹幕视频是一种近年来发展迅速的新兴媒体传播形式,备受大众喜爱,但根据调查显示,部分用户认为弹幕严重影响了视频观看。为改善弹幕视频服务,用户体验质量(Quality of Experience,QoE)的评估迫在眉睫。然而,现有用户体验质量评价方法存在主观因素导致的虚假反馈、反馈不够及时、数据度量关系难以衡量等局限性。在这种情况下,脑电图(electroencephalogram,EEG)因具有无法伪装、高时间分辨率、数据更具统计学意义等优势,已经初步应用于视听刺激的主观评估。结合以上优势,本文提出将EEG突破性地应用于弹幕视频的QoE评估。本研究基于相锁值构建了功能连接特征脑网络,提取了高/低两种QoE水平下的成对差异网络,并基于该网络通过机器学习方法构建评估模型。该评估模型平均分类准确率达到80%,揭示了对于不同类型视频用户产生不同QoE时大脑区域协作的变化模式,并提出与QoE高度相关的额叶为主要会聚区域。以上研究结果表明,该评估模型能真实记录用户观看视频时的生理和心理活动,研究结果为改善弹幕视频服务提供了神经生理学依据。展开更多
目的探讨视频脑电图(VEEG)监测对小儿中枢神经系统感染性疾病鉴别诊断价值及对患儿预后的预测价值。方法选取2020年5月至2021年5月新疆医科大学第一附属医院收治的92例疑似中枢神经系统感染患儿为研究对象,患儿入院后均接受临床病理学...目的探讨视频脑电图(VEEG)监测对小儿中枢神经系统感染性疾病鉴别诊断价值及对患儿预后的预测价值。方法选取2020年5月至2021年5月新疆医科大学第一附属医院收治的92例疑似中枢神经系统感染患儿为研究对象,患儿入院后均接受临床病理学诊断、24 h VEEG监测。以临床病理学诊断结果为金标准,采用Kappa分析法评估VEEG监测结果与临床病理学诊断结果的一致性。根据临床病理诊断结果将患儿分为感染组、未感染组,比较两组患儿VEEG差异。对感染组VEEG异常患儿进行6个月的跟踪随访,根据认知功能评价、后遗症发生情况将患儿分为预后良好组、预后不良组,绘制受试者工作特征(ROC)曲线评估VEEG监测对小儿中枢神经系统感染性疾病的诊断价值,采用线性趋势检验法评估VEEG异常程度对患儿预后的预测价值。结果在92例疑似中枢神经系统感染患儿中,经临床病理学诊断,62例确诊中枢神经系统感染性疾病,其中病毒脑膜炎15例、化脓性脑膜炎24例、脑微循环障碍23例;经VEEG监测,60例确诊中枢神经系统感染性疾病,VEEG监测结果与临床病理学诊断结果的一致性良好(Kappa=0.951,P<0.001)。感染组患儿VEEG监测结果均显示异常,其中轻度异常占43.55%(27/62),中度异常占35.48%(22/62),重度异常占20.97%(13/62),未感染组患儿仅2例出现脑电图轻度异常,占6.67%(2/30),感染组VEEG监测结果异常占比明显高于未感染组(χ^(2)=83.183,P<0.001)。根据临床脑电图进行诊断,结果显示,以δ波广泛持续增多为主的VEEG重度异常患儿均表现为病毒性脑炎,以背景波形变慢为主的VEEG轻度异常、以广泛持续δ波为主的VEEG重度异常患者表现为病毒性脑炎、化脓性脑膜炎或脑微循环障碍,上述三种疾病VEEG改变缺乏特异性。随访6个月,感染组VEEG轻度异常、中度异常、重度异常患儿预后不良发生率分别为22.22%(6/27)、59.09%(13/22)、76.92%(10/13),线性趋势检验结果显示,感染组VEEG异常程度越严重,患儿预后越差(χ^(2)=-12.624,P=0.002)。ROC曲线结果显示,VEEG监测预测小儿中枢神经系统感染性疾病的灵敏度为90.77%,特异度为69.15%,阳性预测值为100.00%,阴性预测值为93.75%,ROC曲线下面积为0.801。结论VEEG监测诊断小儿中枢神经系统感染性疾病的阳性率较高,可为小儿中枢神经系统感染性疾病鉴别、预后评估提供可靠的依据。展开更多
Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning...Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning models to achieve STVSR.They first interpolate intermediate frame features between given frames,then perform local and global refinement among the feature sequence,and finally increase the spatial resolutions of these features.However,in the most important feature interpolation phase,they only capture spatial-temporal information from the most adjacent frame features,ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity.In this paper,we propose a novel long-term temporal feature aggregation network(LTFA-Net)for STVSR.Specifically,we design a long-term mixture of experts(LTMoE)module for feature interpolation.LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features,which are then combined with different weights to obtain interpolation results using several gating nets.Next,we perform local and global feature refinement using the Locally-temporal Feature Comparison(LFC)module and bidirectional deformable ConvLSTM layer,respectively.Experimental results on two standard benchmarks,Adobe240 and GoPro,indicate the effectiveness and superiority of our approach over state of the art.展开更多
基金the National Natural Science Foundation of China under grants 61901078,61871062,and U20A20157in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)under grant 2021FNA04008+5 种基金in part by the China Postdoctoral Science Foundation under grant 2022MD713692in part by the Chongqing Postdoctoral Science Special Foundation under grant 2021XM2018in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyj-zdxmX0024in part by University Innovation Research Group of Chongqing under grant CXQT20017in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under grant SCIE-QN-2022-04.
文摘In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.
基金supported in part by the CAMS Innovation Fund for Medical Sciences,China(No.2019-I2M5-016)National Natural Science Foundation of China(No.62172246)+1 种基金the Youth Innovation and Technology Support Plan of Colleges and Universities in Shandong Province,China(No.2021KJ062)National Science Foundation of USA(Nos.IIS-1715985 and IIS1812606).
文摘Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from the given sequence.Although such a learning scheme is generally effective,it has a critical limitation,i.e.,the model learned on sparse frames only possesses weak generalization ability.This situation could become worse on“long”videos since they tend to have intensive scene variations.Moreover,in such videos,the keyframe information from a longer time span is less relevant to the previous,which could also cause learning conflict and deteriorate the model performance.Thus,the learning scheme is usually incapable of handling complex pattern modeling.To solve this problem,we propose a divide-and-conquer framework,which can convert a complex problem domain into multiple simple ones.First,we devise a novel background consistency analysis(BCA)which effectively divides the mined frames into disjoint groups.Then for each group,we assign an individual deep model on it to capture its key attribute during the fine-tuning phase.During the testing phase,we design a model-matching strategy,which could dynamically select the best-matched model from those fine-tuned ones to handle the given testing frame.Comprehensive experiments show that our method can adapt severe background appearance variation coupling with object movement and obtain robust saliency detection compared with the previous scheme and the state-of-the-art methods.
文摘弹幕视频是一种近年来发展迅速的新兴媒体传播形式,备受大众喜爱,但根据调查显示,部分用户认为弹幕严重影响了视频观看。为改善弹幕视频服务,用户体验质量(Quality of Experience,QoE)的评估迫在眉睫。然而,现有用户体验质量评价方法存在主观因素导致的虚假反馈、反馈不够及时、数据度量关系难以衡量等局限性。在这种情况下,脑电图(electroencephalogram,EEG)因具有无法伪装、高时间分辨率、数据更具统计学意义等优势,已经初步应用于视听刺激的主观评估。结合以上优势,本文提出将EEG突破性地应用于弹幕视频的QoE评估。本研究基于相锁值构建了功能连接特征脑网络,提取了高/低两种QoE水平下的成对差异网络,并基于该网络通过机器学习方法构建评估模型。该评估模型平均分类准确率达到80%,揭示了对于不同类型视频用户产生不同QoE时大脑区域协作的变化模式,并提出与QoE高度相关的额叶为主要会聚区域。以上研究结果表明,该评估模型能真实记录用户观看视频时的生理和心理活动,研究结果为改善弹幕视频服务提供了神经生理学依据。
文摘目的探讨视频脑电图(VEEG)监测对小儿中枢神经系统感染性疾病鉴别诊断价值及对患儿预后的预测价值。方法选取2020年5月至2021年5月新疆医科大学第一附属医院收治的92例疑似中枢神经系统感染患儿为研究对象,患儿入院后均接受临床病理学诊断、24 h VEEG监测。以临床病理学诊断结果为金标准,采用Kappa分析法评估VEEG监测结果与临床病理学诊断结果的一致性。根据临床病理诊断结果将患儿分为感染组、未感染组,比较两组患儿VEEG差异。对感染组VEEG异常患儿进行6个月的跟踪随访,根据认知功能评价、后遗症发生情况将患儿分为预后良好组、预后不良组,绘制受试者工作特征(ROC)曲线评估VEEG监测对小儿中枢神经系统感染性疾病的诊断价值,采用线性趋势检验法评估VEEG异常程度对患儿预后的预测价值。结果在92例疑似中枢神经系统感染患儿中,经临床病理学诊断,62例确诊中枢神经系统感染性疾病,其中病毒脑膜炎15例、化脓性脑膜炎24例、脑微循环障碍23例;经VEEG监测,60例确诊中枢神经系统感染性疾病,VEEG监测结果与临床病理学诊断结果的一致性良好(Kappa=0.951,P<0.001)。感染组患儿VEEG监测结果均显示异常,其中轻度异常占43.55%(27/62),中度异常占35.48%(22/62),重度异常占20.97%(13/62),未感染组患儿仅2例出现脑电图轻度异常,占6.67%(2/30),感染组VEEG监测结果异常占比明显高于未感染组(χ^(2)=83.183,P<0.001)。根据临床脑电图进行诊断,结果显示,以δ波广泛持续增多为主的VEEG重度异常患儿均表现为病毒性脑炎,以背景波形变慢为主的VEEG轻度异常、以广泛持续δ波为主的VEEG重度异常患者表现为病毒性脑炎、化脓性脑膜炎或脑微循环障碍,上述三种疾病VEEG改变缺乏特异性。随访6个月,感染组VEEG轻度异常、中度异常、重度异常患儿预后不良发生率分别为22.22%(6/27)、59.09%(13/22)、76.92%(10/13),线性趋势检验结果显示,感染组VEEG异常程度越严重,患儿预后越差(χ^(2)=-12.624,P=0.002)。ROC曲线结果显示,VEEG监测预测小儿中枢神经系统感染性疾病的灵敏度为90.77%,特异度为69.15%,阳性预测值为100.00%,阴性预测值为93.75%,ROC曲线下面积为0.801。结论VEEG监测诊断小儿中枢神经系统感染性疾病的阳性率较高,可为小儿中枢神经系统感染性疾病鉴别、预后评估提供可靠的依据。
文摘Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning models to achieve STVSR.They first interpolate intermediate frame features between given frames,then perform local and global refinement among the feature sequence,and finally increase the spatial resolutions of these features.However,in the most important feature interpolation phase,they only capture spatial-temporal information from the most adjacent frame features,ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity.In this paper,we propose a novel long-term temporal feature aggregation network(LTFA-Net)for STVSR.Specifically,we design a long-term mixture of experts(LTMoE)module for feature interpolation.LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features,which are then combined with different weights to obtain interpolation results using several gating nets.Next,we perform local and global feature refinement using the Locally-temporal Feature Comparison(LFC)module and bidirectional deformable ConvLSTM layer,respectively.Experimental results on two standard benchmarks,Adobe240 and GoPro,indicate the effectiveness and superiority of our approach over state of the art.
