Abstract
A packet-layer video quality assessment (VQA) model is a lightweight model that predicts the video quality impacted by network conditions and coding configuration for application scenarios such as video system planning and in-service video quality monitoring. It is under standardization in ITU-T Study Group (SG) 12. In this article, we first differentiate the requirements for VQA model from the two application scenarios, and state the argument that the dataset for evaluating the quality monitoring model should be more challenging than that for system planning model. Correspondingly, different criteria and approaches are used for constructing the test datasets, for system planning (dataset-1) and for video quality monitoring (dataset-2), respectively. Further, we propose a novel video quality monitoring model by estimating the spatiotemporal complexity of video content. The model takes into account the interactions among content features, the error concealment effectiveness, and error propagation effects. Experiment results demonstrate that the proposed model achieves robust performance improvement compared with the existing peer VQA metrics on both dataset-1 and dataset-2. It is noted that on the more challenging dataset-2 for video quality monitoring, we obtain a large increase in Pearson correlation from 0.75 to 0.92 and a decrease in the modified RMSE from 0.41 to 0.19.
Highlights
With the development of video service delivery over IP networks, there is a growing interest in low-complexity no-reference video quality assessment (VQA) models for measuring the impact of transmission losses on the perceived video quality
Experimental results First, we compare the correlation between the subjective mean opinion score (MOS) and some affecting parameters that are used in the existing packet-layer models
In order to fairly compare the performance of the above parameters that reflect transmission impairment, the coding artifacts are prevented by properly setting quantization parameter (QP) in our datasets
Summary
With the development of video service delivery over IP networks, there is a growing interest in low-complexity no-reference video quality assessment (VQA) models for measuring the impact of transmission losses on the perceived video quality. We design the respective criteria and methods to select the processed video sequences (PVSs) for subjective evaluation when setting up the subjective mean opinion score (MOS) database This helps us to explain why the abovementioned parametric packet-layer models had a high performance even if the video content feature was not taken into consideration. 3. subjective dataset and analysis As described above, the packet-layer video QoE assessment model has two typical application scenarios, video system planning and in-service video quality monitoring, each of which has different requirements. The objective model for monitoring purpose should be able to estimate as accurately as possible the video quality of each specific sequence distorted by packet loss. To improve prediction accuracy of packet-layer VQA model in the quality monitoring case, influence from video content property, EC strategy, and error propagation should be taken into consideration as much as possible. Thrdsmooth is set to 200 bytes for CIF format sequences coded with H.264 encoder and QP equal to 28
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