Content-Adaptive Packet-Layer Model for Quality Assessment of Networked Video Services
Content-Adaptive Packet-Layer Model for Quality Assessment of Networked Video Services
复制标题
用于网络视频服务质量评估的内容自适应数据包层模型
DOI：
10.1109/jstsp.2012.2207705
复制
发表时间：
2012-10-01
影响因子：
7.5
通讯作者：
Wu, Hong Ren
中科院分区：
文献类型：
--
作者：
Yang, Fuzheng;Song, Jiarun;Wu, Hong Ren
数据包层模型设计为仅使用数据包报头提供的信息，对网络视频服务进行实时和非侵入式质量监控。提出了一种用于网络视频质量评估的内容自适应包层（CAPL）模型。考虑到网络视频的质量下降显著依赖于视频内容的时间和空间特性，时间复杂性被纳入到所提出的模型中。由于直接从数据包报头中获得的信息非常有限，因此在CAPL模型中采用了一种简单且自适应的帧类型检测方法。时间复杂度的估计使用编码P和I帧的比特数的比率。估计的时间复杂度和帧类型被纳入CAPL模型与有关的比特数和丢失的数据包的位置的信息，以获得每个帧的质量估计，通过评估压缩和数据包丢失引起的失真。一个两级的时间池被用来获得给定的帧质量的视频质量。利用内容相关信息，该模型能够适应不同的视频内容。实验结果表明，CAPL模型在均方根误差（RMSE）、皮尔逊相关系数（PCC）、斯皮尔曼秩序相关系数（SCC）和离群值比率（OR）等性能指标上均优于G. 1070模型和DT模型。
Packet-layer models are designed to use only the information provided by packet headers for real-time and non-intrusive quality monitoring of networked video services. This paper proposes a content-adaptive packet-layer (CAPL) model for networked video quality assessment. Considering the fact that the quality degradation of a networked video significantly relies on the temporal as well as the spatial characteristics of the video content, temporal complexity is incorporated in the proposed model. Due to very limited information directly available from packet headers, a simple and adaptive method for frame type detection is adopted in the CAPL model. The temporal complexity is estimated using the ratio of the number of bits for coding P and I frames. The estimated temporal complexity and frame type are incorporated in the CAPL model together with the information about the number of bits and positions of lost packets to obtain the quality estimate for each frame, by evaluating the distortions induced by both compression and packet loss. A two-level temporal pooling is employed to obtain the video quality given the frame quality. Using content related information, the proposed model is able to adapt to different video contents. Experimental results show that the CAPL model significantly outperforms the G.1070 model and the DT model in terms of widely used performance criteria, including the Root-Mean-Squared Error (RMSE), the Pearson Correlation Coefficient (PCC), the Spearman Rank Order Correlation Coefficient (SCC), and the Outlier Ratio (OR).