Packet-layer Model Research Articles

Quality Monitor for 3-D Video Over Hybrid Broadcast Networks

Hybrid broadcast networks are particularly envisaged to merge broadcast TV with broadband Internet and to act as a key enabler for new and better video services in the near future. This is expected to contribute for the evolution of 3-D and multiview video services due to the inherent diversity of its coded data, comprising several complementary streams. Using the multiview video-plus-depth format, at least two independent streams may be delivered through different channels over a hybrid network, that is, broadcasting backward compatible 2-D video in one channel and delivering its corresponding depth stream through complementary channels like LTE-based broadband Internet accesses. This article addresses the problem of monitoring the quality of 3-D video (color plus depth) delivered in such hybrid networking environments, proposing a novel scheme to estimate the impact of visual quality degradation resulting from packet losses in the broadband Internet carrying only the depth stream, without relying on the texture component of the video or any other reference data. A novel non-reference (NR) approach is described, operating as a cascade of two estimators, using only header information of the packets carrying the depth stream through IP broadband. The two-stage cascaded estimator comprises an NR packet-layer model based on an artificial neural network followed by a logistic model, with each stage outputting a separate quality estimate. Performance evaluations, done by comparing the actual and estimated scores for the structural similarity index and subjective differential mean-opinion score, reveals high accuracy for both of these estimates, with Pearson linear correlation coefficient values greater than 0.89. Since only packet-layer information is used, the algorithmic complexity of this monitoring tool is low, making it suitable for standalone implementation at arbitrary network nodes.

Packet-Layer Quality Assessment for Networked Video

To realize real-time and non-intrusive quality monitoring for networked video, a content-adaptive packet-layer model for quality assessment is proposed. Considering the fact that the coding distortion of a video is dependent not only on the bit-rate but also on the motion characteristic of the video content, temporal complexity is evaluated and incorporated in quality assessment in the proposed model. Since very limited information is available for a packet-layer model, an adaptive method for frame type detection is first applied. Then the temporal complexity which reflects the motion characteristic of the video content is estimated using the ratio of the bit-rate for coding I frames and P frames. The estimated temporal complexity is incorporated in the proposed model, making it adaptive to different video content. Experimental results show that the proposed model achieves an advanced performance in comparison with the ITU-T G.1070 model.

Content-Adaptive Packet-Layer Model for Quality Assessment of Networked Video Services

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).

No Reference Video-Quality-Assessment Model for Monitoring Video Quality of IPTV Services

Service providers should monitor the quality of experience of a communication service in real time to confirm its status. To do this, we previously proposed a packet-layer model that can be used for monitoring the average video quality of typical Internet protocol television content using parameters derived from transmitted packet headers. However, it is difficult to monitor the video quality per user using the average video quality because video quality depends on the video content. To accurately monitor the video quality per user, a model that can be used for estimating the video quality per video content rather than the average video quality should be developed. Therefore, to take into account the impact of video content on video quality, we propose a model that calculates the difference in video quality between the video quality of the estimation-target video and the average video quality estimated using a packet-layer model. We first conducted extensive subjective quality assessments for different codecs and video sequences. We then model their characteristics based on parameters related to compression and packet loss. Finally, we verify the performance of the proposed model by applying it to unknown data sets different from the training data sets used for developing the model.

Quality of Experience Evaluation for Networked Video Services

Rapid development of network technologies has led to a proliferation of networked video applications, making efficient and effective assessment of the related Quality of (user) Experience (QoE) an acute need. In literature, there has been a non-uniform view of Quality of Service (QoS) and therefore the concept of QoE is introduced to avoid the confusion. The difference between QoS and QoE is first clarified in this paper. Then QoE metrics in the current standardized framework for Internet Protocol (IP) based streaming applications in 3G networks, i.e., 3GPP PSS, are introduced. Moreover, objective QoE evaluation for networked video is addressed in respect to perceived video quality, which can be categorized into the parametric-planning model, parametric packet-layer model, parametric bit-stream-layer model, media-layer model, and hybrid model. The related characteristics, state-of-the-art, standardization process and key patents are respectively reviewed.

Parametric Packet-Layer Model for Evaluation Audio Quality in Multimedia Streaming Services

We propose a parametric packet-layer model for monitoring audio quality in multimedia streaming services such as Internet protocol television (IPTV). This model estimates audio quality of experience (QoE) on the basis of quality degradation due to coding and packet loss of an audio sequence. The input parameters of this model are audio bit rate, sampling rate, frame length, packet-loss frequency, and average burst length. Audio bit rate, packet-loss frequency, and average burst length are calculated from header information in received IP packets. For sampling rate, frame length, and audio codec type, the values or the names used in monitored services are input into this model directly. We performed a subjective listening test to examine the relationships between these input parameters and perceived audio quality. The codec used in this test was the Advanced Audio Codec-Low Complexity (AAC-LC), which is one of the international standards for audio coding. On the basis of the test results, we developed an audio quality evaluation model. The verification results indicate that audio quality estimated by the proposed model has a high correlation with perceived audio quality.

Non-intrusive Packet-Layer Model for Monitoring Video Quality of IPTV Services

Developing a non-intrusive packet-layer model is required to passively monitor the quality of experience (QoE) during service. We propose a packet-layer model that can be used to estimate the video quality of IPTV using quality parameters derived from transmitted packet headers. The computational load of the model is lighter than that of the model that takes video signals and/or video-related bitstream information such as motion vectors as input. This model is applicable even if the transmitted bitstream information is encrypted because it uses transmitted packet headers rather than bitstream information. For developing the model, we conducted three extensive subjective quality assessments for different encoders and decoders (codecs), and video content. Then, we modeled the subjective video quality assessment characteristics based on objective features affected by coding and packet loss. Finally, we verified the model's validity by applying our model to unknown data sets different from training data sets used above.