FrameLoss Adaptive Temporal Pooling for Video Quality Assessment In this paper a frameloss adaptive temporal pooling method for video quality assessment is proposed. Extensive subjective tests have been carried out to determine the duration of successive frames based on which steady quality judgment can be made by human observers. The resulting duration is applied to the determination of the length of Group of Frames (GOF), where a flexible algorithm is used to separate the input video into variable sized GOFs. Shortterm temporal pooling is first performed for each of the GOF to get the GOF quality, where quality contribution of each frame is incorporated with the context and frame loss well taken into account. The video quality is then obtained by longterm temporal pooling of the GOF quality considering the fact that perceptual video quality is predominately determined by the worst parts of the video. Extensive experimental results have demonstrated the effectiveness of the proposed method both for regular and irregular frame loss.

A PacketLayer Video Quality Assessment Model Based on Spatiotemporal Complexity Estimation Packetlayer video quality assessment model is a lightweight model that is useful for application scenarios like inservice video quality monitoring and network service planning, which is under standardization in ITUT. In this paper we first differentiate the requirements for video quality assessment model from these two different application scenarios. Then different criteria and methods are analyzed and used to construct two types of test datasets for model building and evaluation. Finally, a novel packetlayer video quality assessment model dealing with video quality monitoring is proposed by incorporating the estimation of the spatiotemporal complexity of video content. The interaction between content features and the error concealment effects plus error propagation effects is considered. Experiment results demonstrate that the proposed model achieves preferable and robust performance improvement compared with the existing models in both datasets for scenarios of video quality planning and monitoring. Especially, larger Pearson correlation increase from 0.75 to 0.93 and RMSE decrease from 0.56 to 0.31 are obtained in dataset for video quality monitoring.