Chadha, Aaron;
Katsavounidis, Ioannis;
Bhunia, Ayan Kumar;
Stejerean, Cosmin;
Khan, Mohammad Umar;
Andreopoulos, Yiannis;
(2022)
Domain-Specific Fusion Of Objective Video Quality Metrics.
In:
Proceedings of the 30th ACM International Conference on Multimedia.
(pp. pp. 1387-1395).
Association for Computing Machinery (ACM): New York, NY, United States.
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Abstract
Video processing algorithms like video upscaling, denoising, and compression are now increasingly optimized for perceptual quality metrics instead of signal distortion. This means that they may score well for metrics like video multi-method assessment fusion (VMAF), but this may be because of metric overfitting. This imposes the need for costly subjective quality assessments that cannot scale to large datasets and large parameter explorations. We propose a methodology that fuses multiple quality metrics based on small scale subjective testing in order to unlock their use at scale for specific application domains of interest. This is achieved by employing pseudo-random sampling of the resolution, quality range and test video content available, which is initially guided by quality metrics in order to cover the quality range useful to each application. The selected samples then undergo a subjective test, such as ITU-T P.910 absolute categorical rating, with the results of the test postprocessed and used as the means to derive the best combination of multiple objective metrics using support vector regression. We showcase the benefits of this approach in two applications: video encoding with and without perceptual preprocessing, and deep video denoising & upscaling of compressed content. For both applications, the derived fusion of metrics allows for a more robust alignment to mean opinion scores than a perceptually-uninformed combination of the original metrics themselves. The dataset and code is available at https://github.com/isize-tech/VideoQualityFusion.
| Type: | Proceedings paper |
|---|---|
| Title: | Domain-Specific Fusion Of Objective Video Quality Metrics |
| Event: | 30th ACM International Conference on Multimedia |
| ISBN-13: | 9781450392037 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3503161.3548375 |
| Publisher version: | https://doi.org/10.1145/3503161.3548375 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | video denoising, datasets, video coding, quality assessment, neural networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10170819 |
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