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 human...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 32; pp. 6004 - 6019
Main Authors Danier, Duolikun, Zhang, Fan, Bull, David R.
Format Journal Article
LanguageEnglish
Published New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Benchmark testing
BVI-VFI
Image processing
Image quality
Interpolation
Kernel
Measurement
perceptual quality
Quality assessment
Spatial databases
subjective quality assessment
Video
video frame interpolation
Video quality database
Online AccessGet full text
ISSN1057-7149
1941-0042
1941-0042
DOI10.1109/TIP.2023.3327912

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Summary: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 .
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2023.3327912