BVI-VFI: A Video Quality Database for Video Frame Interpolation

Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the development of more advanced VFI algorithms has been extensively researched, there remains little understanding of how humans perceive the quality of interpolated content and how well existing objective quality assessment methods perform when measuring the perceived quality. In order to narrow this research gap, we have developed a new video quality database named BVI-VFI, which contains 540 distorted sequences generated by applying five commonly used VFI algorithms to 36 diverse source videos with various spatial resolutions and frame rates. We collected more than 10,800 quality ratings for these videos through a large scale subjective study involving 189 human subjects. Based on the collected subjective scores, we further analysed the influence of VFI algorithms and frame rates on the perceptual quality of interpolated videos. Moreover, we benchmarked the performance of 33 classic and state-of-the-art objective image/video quality metrics on the new database, and demonstrated the urgent requirement for more accurate bespoke quality assessment methods for VFI. To facilitate further research in this area, we have made BVI-VFI publicly available at https://github.com/danier97/BVI-VFI-database

Q-AIMD: A Congestion Aware Video Quality Control Mechanism

Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called Q-AIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, Q-AIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them

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Video Quality Assessment: Exploring the Impact of Frame Rate

Technology advancements in the past decades has led to an immense increase in data traffic over various networks. Videos constitute a major percentage of this traffic and their share is projected to increase at an accelerating speed in the coming years. Service providers aim to deliver videos that have high quality while at the same time keeping the data rate as low as possible. Effective and efficient objective Video Quality Assessment~(VQA) algorithms are essential in order to provide real time estimate of video quality so that the best compromise between data rate and quality can be achieved. Data rate of video transmission can be altered by controlling different parameters of the video, among which frame rate is one of the most important parameters. So far, only limited works have been done to study the impact of frame rate variations on video quality. The purpose of this work is to investigate the impact of varying frame rate on the quality of videos and to develop novel VQA models that integrate frame rate variations into the task of quality assessment. In order to achieve this goal, we first construct two new video databases that contain videos of diverse content, and spatial and temporal resolutions. We carry out subjective studies on these databases to obtain human opinions on video quality. The subjective study allows us to evaluate the performance of well known objective VQA algorithms on cross-frame rate videos. The results reveal that there is considerable disparity between the subjective scores and the predictions from state-of-the-art objective models that do not take frame rate into consideration. We explore statistical models for video quality analysis. In particular, we conduct cross-frame local phase statistical analysis, which provides new insights on video motion smoothness as an important factor that affects video quality across different frame rates. Our evaluations of the proposed motion smoothness metric using the subject-rated databases show that this novel measure provides a new means to capture the impact of frame rate on video quality, and demonstrates strong promise at improving the performance of objective video quality assessment models. We also propose the notions of perceptual temporal aliasing factor and perceptual spatiotemporal aliasing factor to incorporate the characteristics of human visual contrast sensitivity variations into the framework of spatial and temporal aliasing analysis. We incorporate the proposed aliasing factors into the VQA process to predict the quality of video under frame rate change, resolution change, and lossy compression. Our performance evaluation using the subjective database shows that the proposed perceptual aliasing factors are strong quality predictors across-frame rate, resolution, and data rate, significantly outperforming baseline VQA methods without aliasing modeling