Parametric Planning Model for Video Quality Evaluation of IPTV Services Combining Channel and Video Characteristics
Parametric Planning Model for Video Quality Evaluation of IPTV Services Combining Channel and Video Characteristics
复制标题
结合频道和视频特性的IPTV业务视频质量评估参数化规划模型
DOI：
10.1109/tmm.2016.2638621
复制
发表时间：
2017-05-01
影响因子：
7.3
通讯作者：
Gao, Shan
中科院分区：
文献类型：
--
作者：
Song, Jiarun;Yang, Fuzheng;Gao, Shan
参数化规划模型旨在评估视频质量，可应用于网络视频应用和通信网络的有效规划、实施和管理。然而，与基于比特流的评估模型不同，规划模型不允许利用视频流，只能使用有限的信息，即服务提供商和网络运营商预先确定的一些通用参数。本文提出了一种结合频道和视频特性的参数规划模型来估计互联网协议电视（IPTV）服务中丢包引起的视频失真。更具体地，通过对信道特性的详细分析来确定信道状态的概率分布。然后，考虑突发丢包的影响和帧之间的时间依赖性，从信道状态概率分布的角度推导了用于视频质量评估的几个序列级和帧级参数。利用这些参数，所提出的模型在考虑直接数据包丢失和错误传播的影响的情况下近似视频质量。实验结果表明，该模型在视频质量估计方面比三种常用的参数规划模型具有更优越的性能。
Parametric planning models are designed for estimating the video quality, which can be applied to effective planning, implementation, and management of network video applications and communication networks. However, different from the bitstream-based evaluation models, the planning models are not allowed to exploit the video streams, with only limited information available for use, i.e., a few general parameters predetermined by the service providers and network operators. In this paper, a parametric planning model combining channel and video characteristics is proposed to estimate the video distortion caused by packet loss for Internet protocol television (IPTV) services. More specifically, the probability distribution of the channel states is determined by detailed analysis of the channel characteristics. Then, considering the influence of burst packet loss and the temporal dependence between frames, several sequence-level and frame-level parameters for video quality evaluation are derived from the perspective of the probability distribution of the channel states. Utilizing these parameters, the proposed model approximates the video quality considering the effects of direct packet loss and error propagation. Experimental results show that the proposed model has a superior performance for video quality estimation than the three commonly used parametric planning models.