视频编码的技术基础及发展方向

现有视频编码采用基于块的混合编码架构,利用预测、变换、量化和熵编码技术实现对视频信号的高效压缩.在现有架构基础上进一步优化,提供针对视频图像信号局部特性的更加灵活的处理和编码.基于机器学习的视频编码技术有望部分或全面地改变现有的混合编码框架,给视频编码带来新的研究思路.未来视频除了现有的二维平面视频,还需要编码面向AR/VR应用的球面视频数据和体视频数据,这些新的视频源数据格式也给视频编码技术研究带来新的机会和挑战.     

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.     

Research on Q-learning based rate control approach for HTTP adaptive streaming

HTTP adaptive streaming (HAS) has become the standard for adaptive video streaming service.In changing network environments,current hardcoded-based rate adaptation algorithm was less flexible,and it is insufficient to consider the quality of experience (QoE).To optimize the QoE of users,a rate control approach based on Q-learning strategy was proposed.the client environments of HTTP adaptive video streaming was modeled and the state transition rule was defined.Three parameters related to QoE were quantified and a novel reward function was constructed.The experiments were employed by the Q-learning rate control approach in two typical HAS algorithms.The experiments show the rate control approach can enhance the stability of rate switching in HAS clients.     

基于模糊空时线索的多目标在线跟踪算法

多目标在线跟踪是视频监控中的关键问题之一.针对日益增长的智能化视频监控的需求,提出了一种基于模糊空时线索的多目标在线跟踪算法.在该算法中,引入模糊空时多属性特征定义距离函数,利用模糊C均值聚类优化得到交叉隶属度矩阵,实现目标与观测间的数据关联.为了减少错误的轨迹起始,利用空时线索定义了遮挡度函数,判别出新目标并起始相应的目标轨迹.实验结果表明,本文算法能够准确地估计出目标的运动轨迹.本文算法可应用于视频监控、安防以及自动驾驶等领域.     

融合空时感知特性的无参考视频质量评估算法

本文通过简化视频质量评估中人眼感知模型的复杂性,提出了一种新的无参考视频质量评估模型.首先通过分别抽取视频的空间域和时间域特征,然后按照视频局部块、视频帧、视频段等从细到粗的不同粒度,模拟人眼感知特性进行多重加权汇聚,最终得到整段视频的特征向量描述.本方法以支持向量回归器为评估模型训练工具,通过有监督的视频样本库训练,以无参考方式完成未知视频的质量评估.实验结果表明,该评估算法的性能不但要优于当前已知最经典的无参考评估算法Video BLLINDS,而且与部分参考评估算法相当.     

Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing

In multi-hypothesis based distributed compressed video sensing systems,the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However,the acquiring of the hypothesis set has not been concerned in existing works.A reconstruction algorithm based on multi-reference frames hypothesis optimization (MRHO) was proposed.This algorithm expanded the selection of hypothesis vectors by increasing the number of reference frames.The quality of the prediction set was improved by hypotheses optimization selection under the same size with the original hypothesis set.Simulation results show that the proposed MRHO algorithm effectively improves the reconstructed quality of the distributed compressed video sensing scheme.     

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.     

压缩域视频拼接方法研究

本文主要研究实时视频应用的拼接方法并详述了其在云视频会议上的应用.不同于传统的基于转码的视频拼接,提出了基于压缩域码流的处理算法,并在云端架构视频处理器.以HEVC码流为例将码流处理的难点分为序列参数集、图片参数集、Slice头部和编码单元四个方面,对这些方面存在的问题给出了解决方法,从而实现了低复杂度的视频拼接算法.仿真结果表明,本文提出的压缩域视频拼接算法,在视频处理效率和质量上均有很大的提高.     

基于自适应量化矩阵的硬判决量化算法

在视频编码中,视频量化一般分为硬判决量化(HDQ)和软判决量化(SDQ),HDQ与SDQ相比,编码性能虽有所损失,但其编码复杂度低,易于硬件实现的优点依旧是主流编码器所主要采用的量化算法.人眼具有对图像中的高频细节不敏感的特性.因此,基于Bayes最小误判概率约束,离线构建基于视频内容自适应的量化矩阵,在模拟感知SDQ算法机理下,对高频低频分量采用不同的量化步长,提高视频的主观质量和HDQ算法性能.仿真实验表明,相比于传统的HDQ算法,该文算法能达到平均5.048％的码率节省,其中WVGA和WQVGA格式平均达到10.65％的码率节省.相比于感知SDQ算法,平均码率增加仅有1.464％;算法复杂度方面,编码一帧的时间相比于感知SDQ节省了32.956％.     

新媒体平台电视节目高效组织和浏览研究

为实现电视节目的快速传播和高效利用,提出了电视节目的新媒体平台高效组织和浏览技术.首先,通过传统广电媒资数据向新媒体平台的快速迁移实现电视节目的快速发布.然后,采集并分析用户的观看行为,通过基于投票的方法计算出视频热点区域,并添加中插故事点.最后,结合观看行为、用户和节目间相似性等信息,通过改进的协同过滤方法实现电视节目的个性化推荐.系统在多个省、市、县电视台的新媒体部门进行了试运行,结果表明系统操作简单、运行高效、能够显著提升电视节目的观赏体验,具有广泛的应用前景.