Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
Year: 2018
Type: article
Abstract: The recently developed video multi-method assessment fusion (VMAF) framework integrates multiple quality-aware features to accurately predict the video quality. However, the VMAF does not yet exploit important principles of temporal perception that are relevant to the perceptual video distortion measurement. Here, we propose two improvements to the VMAF framework, called spatiotemporal VMAF and ensemble VMAF, which leverage perceptually-motivated space-time features that are efficiently calculated at multiple scales. We also conducted a large subjective video study, which we have found to be an excellent resource for training our feature-based approaches. In rigorous experiments, we found that the proposed algorithms demonstrate the state-of-the-art performance on multiple video applications. The compared algorithms will be made available as a part of the open source package in https://github.com/Netflix/vmaf.
Authors: Christos G. Bampis, Zhi Li, Alan C. Bovik
Institutions: Netflix (United States), The University of Texas at Austin
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2796677919