Visual Quality Assessment after Network Transmission Incorporating NS2 and Evalvid

On the basis of Evalvid tool integrated in NS2 (Network Simulator version 2), the paper gets new set of tools, myEvalvid, to establish the simulation and evaluation platform for multimedia transmission. Then the paper investigates the effects of various influence factors when multimedia information is transmitted in the network and the relationships among these factors. Based on the analysis, the paper gets different evaluation models, respectively. In this paper, we study the impact on performance of several basic source and network parameters of video streams, namely, GOP (Group of Pictures) pattern, compression quantitative parameters, packet length, and packet error rate. Simulation results show that different parameters lead to different distortion levels which are calculated according to the reconstruction images at the receiver and the original images. The experimental results show that the video transmission and quality evaluation model we designed can evaluate multimedia transmission performance over complex environment very well

QoE Models for Online Video Streaming

This is the author accepted manuscript.With the rising popularity of video streaming services, Quality of Experience (QoE) acts as the crucial role in improving user’s experience. Distinguished from Quality of Service (QoS), which mainly weigh the video streaming quality with network transmission performance, QoE focuses on the subjective and user-oriented assessment when users views the videos online. It is a vital issue for online streaming to ensure a user-satisfied transmission under dynamic network. This highlights the need for an accurate and feasible QoE model to balance the user experience and available transmission bandwidth. This paper summaries and analyzes existing explorations on QoE models for online video streaming, especially with the advancement on emerged learning-based models, to promote the development of this research field.National Key Research and Development Program of ChinaEuropean Union Horizon 2020National Natural Science Foundation of China (NSFC)Natural Science Foundation of JiangsuLeading Technology of Jiangsu Basic Research PlanChongqing Key Laboratory of Digital Cinema Art Theory and Technolog

No-Reference Video Quality Assessment Model for Distortion Caused by Packet Loss in the Real-Time Mobile Video Services

Packet loss will make severe errors due to the corruption of related video data. For most video streams, because the predictive coding structures are employed, the transmission errors in one frame will not only cause decoding failure of itself at the receiver side, but also propagate to its subsequent frames along the motion prediction path, which will bring a significant degradation of end-to-end video quality. To quantify the effects of packet loss on video quality, a no-reference objective quality assessment model is presented in this paper. Considering the fact that the degradation of video quality significantly relies on the video content, the temporal complexity is estimated to reflect the varying characteristic of video content, using the macroblocks with different motion activities in each frame. Then, the quality of the frame affected by the reference frame loss, by error propagation, or by both of them is evaluated, respectively. Utilizing a two-level temporal pooling scheme, the video quality is finally obtained. Extensive experimental results show that the video quality estimated by the proposed method matches well with the subjective quality

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)