互动、虚拟与沉浸：助眠类短视频的内容生产机制探讨

随着虚拟现实技术的出现和各大短视频平台的勃兴，具有解压和情绪调节功能的助眠类短视频的热度持续走高，受众辐射范围也愈加广泛。结合视觉文化的发展和社会实践背景，本文通过分析助眠类短视频的内容生产机制，将感官互动、场景模拟、沉浸传播归结为该类短视频内容生产的逻辑内核，以期对未来助眠类短视频的发展有所助益。     

基于社区发现的舆情热点挖掘研究

信息时代的快速发展,让社交网络平台成为人们交流工作、发表意见、联络感情的第二世界。为了准确地在大量网络舆情数据中发现热点并分析其热度,提出了基于社区发现的舆情热点挖掘研究,使用社区发现算法对舆情数据的热点挖掘工作进行研究,鉴于微博数据传播机制的特点,微博话题的热度值会受到转发量、评论数、点赞次数以及发表时间的影响,为此引入微博舆情数据关键词的热度影响因子来提高针对微博舆情数据热度计算的准确率。并在此基础上提出一种基于综合权重计算和热度影响因子的改进算法—CWHIF-TR。构建针对微博文本的热度分析模型,完成热点关键词热度分析实验和摘要句热度分析实验,为热度计算的相关研究提供思路。     

互联网热点视频分析方法研究

经过数年的发展,互联网视频已经成为互联网上的主要应用之一。当前,互联网视频具有数量大、发布快、受众多、影响大的特点。在互联网海量的数据中,如何发现视频节目,如何分析和跟踪热点节目成为非常重要的问题。本文对热点视频的概念进行了分析,对热点视频的发现、采集、跟踪技术和策略等进行了研究,对互联网热点视频分析与跟踪系统的模块进行了设计。     

视频网格中自适应热度变化的条块化存储

针对在基于内容分发网络(Content Delivery Network,CDN)存储机制的视频网格中,用户申请大量未被存储在边缘服务器上的次热度视频文件所造成的传输延迟问题,提出了自适应热度变化的条块化存储机制。在机制中视频文件根据其热度被分块存储、调度和更新,可增加边缘服务器存储的视频文件数量。仿真模拟的结果表明:机制在保证视频服务质量的同时,可以提高用户在本域边缘服务器获得视频服务的命中率。     

基才时间信息的快速检索方法探究

本文研究了利用时间信息实现快速检索视频文件的方法,视频文件的压缩编码标准采用的是H．264,该方法主要包括视频分析和视频检索两个部分的研究。在视频分析部分,对码流中的IDR帧（关键帧）进行抓取和获得其时间、位置、大小等信息。并将得到的时间等信息存入至数据库。该部分的重点是时间信息的获取,由于负责传输码流的RTP数据分组的时间戳记录了码流的时间信息,所以着重描述了获取并且记录RTP时间戳的过程。视频检索部分,利用时间等查询条件从数据库中进行视频检索。对所述方法进行研究。结果表明使用该方法能够快速查看某时刻视频内容．避免了检索视频时的重复处理。     

MPEG-2传输流中Ⅰ-帧的统计分析

Ⅰ-帧为MPEG定义的一种图像格式,它的应用场合比较广泛,比如基于视频内容检索的多媒体系统、视频节目的特技播放,给出一种对MPEG-2传输流中Ⅰ-帧分布的统计方法,对3段数字电视节目进行实验,得到节目流中Ⅰ-帧的分布情况,包括Ⅰ-帧的大小、间隔等,并且分析了TS流中Ⅰ-帧分布的特点.     

MPEG-2传输流中Ⅰ-帧的统计分析

Ⅰ-帧为MPEG定义的一种图像格式,它的应用场合比较广泛,比如基于视频内容检索的多媒体系统、视频节目的特技播放,给出一种对MPEG-2传输流中Ⅰ-帧分布的统计方法,对3段数字电视节目进行实验,得到节目流中Ⅰ-帧的分布情况,包括Ⅰ-帧的大小、间隔等,并且分析了TS流中Ⅰ-帧分布的特点.     

基于时空预测模型的视频码率控制算法

针对传统视频图像码率控制算法不能很好地支持视频内容变化或者场景切换应用问题,提出一种基于时空预测模型的视频码率控制算法.该算法根据视频图像压缩时基于空间关联与时间关联的预测属性,引入人体视觉感知模型,通过对时间通道中的视频内容进行理解,采用视频图像帧间绝对误差均值(Mean Absolute Deviation,MAD)的方差来表征视频图像在时间通道中的运动趋势,评估当前视频图像帧内容复杂度并预测出MAD值;在此基础上,通过时空关联因子对MAD的预测值进行矫正,使得码率控制算法中关键参数MAD值能更好地贴合实际情况.实验证明,采用时空维度分析的码率控制算法更科学、更精准.     

新媒体环境下声乐表演短视频拍摄与创作技巧分析

新媒体环境下,短视频成为大众休闲娱乐和知识学习的重要形式,受到广大互联网用户的喜爱和追捧,新媒体信息传播领域开始通过短视频来进行信息传播、商业营销、品牌宣传等行为,使得短视频的创作热潮兴起,内容也变得十分丰富.声乐表演是短视频内容创作的重要内容,具有广泛的用户基础.目前短视频平台中与声乐表演相关的内容十分丰富,在这样的环境下,要想实现短视频内容的良好传播,就需要根据用户的特点来进行拍摄和创作,提升短视频的整体质量,才能够达到最佳的传播效果.基于此,分析新媒体环境下短视频视听内容的特点,阐述声乐类短视频的拍摄和创作技巧,为声乐表演类短视频的创作提供一些参考建议.     

移动视频业务质量分析系统的设计与实现

移动视频业务客户感知不仅取决于网络质量,更取决于“端管云台”各环节,亟需从基于网络本身的质量管理向注重客户感知的端到端横向一体化质量保障机制转变.为解决这些问题,介绍一个移动视频业务质量分析系统的设计及其实现.系统以移动视频业务各环节的信令消息为分析数据源;从业务、流向、地域、网元、小区、终端6个维度建立分析模型;呈现视频业务的关键KQI值及变化趋势,常态化开展性能指标监控;从用户视角发现问题,对精细化业务、区域等角度的性能劣化指标进行趋势分析、质差分析、定界分析;对发现的问题进行自动或人工派单处理,建立完备的问题闭环管理机制.该系统能够显著提升移动视频质量管理工作水平,并显著降低工作成本.对系统的架构、主要功能部件的实现进行了详细的介绍,对系统的应用进行了举例说明.     

Analyzing the dynamics of online video popularity

Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects: overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion of active days is proposed, and the per-day and per-hour popularity distributions of individual videos are carefully studied. Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.     

Watching user generated videos with prefetching

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by performing a PlanetLab-based measurement demonstrating that video playback on YouTube is often unsatisfactory and introduce a series of prefetching schemes: (1) the conventional caching scheme, which caches all the videos that users have watched, (2) the search result-based prefetching scheme, which prefetches videos that are in the search results of users' search queries, and (3) the recommendation-aware prefetching scheme, which prefetches videos that are in the recommendation lists of the videos that users watch. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio of up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates strong potential for improving the playback quality at the client. In addition, we explore the trade-offs and feasibility of implementing recommendation-aware prefetching.     

Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems

User generated content (UGC), now with millions of video producers and consumers, is reshaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world's largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show how UGC services are fundamentally different from traditional VoD services. We then analyze the intrinsic statistical properties of UGC popularity distributions and discuss opportunities to leverage the latent demand for niche videos (or the so-called "the Long Tail" potential), which is not reached today due to information filtering or other system scarcity distortions. Based on traces collected across multiple days, we study the popularity lifetime of UGC videos and the relationship between requests and video age. Finally, we measure the level of content aliasing and illegal content in the system and show the problems aliasing creates in ranking the video popularity accurately. The results presented in this paper are crucial to understanding UGC VoD systems and may have major commercial and technical implications for site administrators and content owners.     

动态扩容视频存储分发系统设计

在带宽增速,互联网用户对视频内容的要求日趋增多的趋势下,本文描述了大规模视频存储分发的系统设计和实现.该系统设计的目标是把用户上传的视频重新编解码生成适合不同场景的不同格式视频文件,然后根据客户端支持的视频属性和网络带宽的实际情况,自动分发不同码流的视频文件到客户端,从而达到流畅访问视频的目的;对系统中的热门视频,系统通过算法结合缓存,实现同一视频文件在不同服务器间动态迁移,从而充分利用系统的计算能力,最终给用户提供更流畅的视频体验.     

基于神经网络与马尔可夫组合模型的视频流行度预测算法

为了提升用户体验,降低运营商的成本,将播放最多的视频内容提前放入用户侧缓存是业界的通用做法,如何有效预测视频播放热度已经成为业界热点问题。针对传统预测算法非线性映射能力差、预测精度低及自适应性弱等缺点,提出基于神经网络与马尔可夫组合模型的视频流行度预测算法（Mar-BiLSTM）,该算法通过构建双向长短期记忆（bi-directional long short-term memory,BiLSTM）网络模型可以保留时间序列两个方向的信息依赖;同时在避免引入外部变量导致模型复杂度增加的情况下,利用马尔可夫性质进一步提高了模型的预测精度。实验结果表明,与传统的时间序列和经典的神经网络算法相比,所提算法提升了视频流行度预测的准确性、时效性,并降低了计算量。     

Cache‐at‐relay: energy‐efficient content placement for next‐generation wireless relays

Uploading and downloading content have recently become one of the major reasons for the growth of Internet traffic volume. With the increasing popularity of social networking tools and their video upload/download applications, as well as the connectivity enhancements in wireless networks, it has become a second nature for mobile users to access on‐demand content on‐the‐go. Urban hot spots, usually implemented via wireless relays, answer the bandwidth need of those users. On the other hand, the same popular contents are usually acquired by a large number of users at different times, and fetching those from the initial content source each and every time makes inefficient use of network resources. In‐network caching provides a solution to this problem by bringing contents closer to the users. Although in‐network caching has been previously studied from latency and transport energy minimization perspectives, energy‐efficient schemes to prolong user equipment lifetime have not been considered. To address this problem, we propose the cache‐at‐relay (CAR) scheme, which utilizes wireless relays for in‐network caching of popular contents with content access and caching energy minimization objectives. CAR consists of three integer linear programming models, namely, select relay, place content, and place relay, which respectively solve content access energy minimization, joint minimization of content access and caching energy, and joint minimization of content access energy and relay deployment cost problems. We have shown that place relay significantly minimizes the content access energy consumption of user equipments, while place content provides a compromise between the content access and the caching energy budgets of the network. Copyright © 2015 John Wiley & Sons, Ltd.     

An objective and subjective quality assessment study of passive gaming video streaming

Passive gaming video‐streaming applications have recently gained much attention as evident with the rising popularity of many Over The Top (OTT) providers such as Twitch.tv and YouTube Gaming. For the continued success of such services, it is imperative that the user Quality of Experience (QoE) remains high, which is usually assessed using subjective and objective video quality assessment methods. Recent years have seen tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. A study on the performance of objective VQA on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos, is still missing. Towards this end, we present in this paper an objective and subjective quality assessment study on gaming videos considering passive streaming applications. Subjective ratings are obtained for 90 stimuli generated by encoding six different video games in multiple resolution‐bitrate pairs. Objective quality performance evaluation considering eight widely used VQA metrics is performed using the subjective test results and on a data set of 24 reference videos and 576 compressed sequences obtained by encoding them in 24 resolution‐bitrate pairs. Our results indicate that Video Multimethod Assessment Fusion (VMAF) predicts subjective video quality ratings the best, while Naturalness Image Quality Evaluator (NIQE) turns out to be a promising alternative as a no‐reference metric in some scenarios.     

Resource assignment in short video accelerator system

Video accelerator is developed for better user experience in video sharing websites such as YouTube.PPLive video accelerator (PPVA),which has the largest number of users in China,is based on peer-to-pe...     

基于显著特征增强的跨模态视频片段检索

随着视频获取设备和技术的不断发展,视频数量增长快速,在海量视频中精准查找目标视频片段是具有挑战的任务。跨模态视频片段检索旨在根据输入一段查询文本,模型能够从视频库中找出符合描述的视频片段。现有的研究工作多是关注文本与候选视频片段的匹配,忽略了视频上下文的“语境”信息,在视频理解时,存在对特征关系表达不足的问题。针对此,该文提出一种基于显著特征增强的跨模态视频片段检索方法,通过构建时间相邻网络学习视频的上下文信息,然后使用轻量化残差通道注意力突出视频片段的显著特征,提升神经网络对视频语义的理解能力。在公开的数据集TACoS和ActivityNet Captions的实验结果表明,该文所提方法能更好地完成视频片段检索任务,比主流的基于匹配的方法和基于视频-文本特征关系的方法取得了更好的表现。     

Characterization and modeling of video popularity

Popularity of videos is a key factor for the design and management of a streaming media system. In this paper, three factors representing clients’ access to a media system are investigated to characterize popularity. One of the factors is the commonly used access frequency. Numerical studies on one of the workloads from a video‐on‐demand system in the University of Science and Technology of China are also given. The results show that the commonly used Zipf‐like model is not suitable to characterize popularity distribution. However, the stretched exponential model is better, and the shape of the stretched exponential distribution of popularity is related to the characterization of popularity and the duration of workload. The rank correlation between the access frequency and the other two factors is further studied. It is concluded that the rank of video popularity and the ability of each characterization to distinguish video popularity are different, which implies that the combinative use of different characterizations can increase the distinction of video popularity.Copyright © 2013 John Wiley & Sons, Ltd.