Enhancing VMAF through New Feature Integration and Model Combination

Fan Zhang, Angeliki Katsenou, Christos Bampis, Lukas Krasula, Zhi Li, David Bull

Research output: Contribution to journalArticle (Academic Journal)

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Abstract

VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through SVM regression. It offers higher correlation with subjective opinions compared to many conventional quality assessment methods. In this paper we propose enhancements to VMAF through the integration of new video features and alternative quality metrics (selected from a diverse pool) alongside multiple model combination. The proposed combination approach enables training on multiple databases with varying content and distortion characteristics. Our enhanced VMAF method has been evaluated on eight HD video databases, and consistently outperforms the original VMAF model (0.6.1) and other benchmark quality metrics, exhibiting higher correlation with subjective ground truth data.
Original languageEnglish
Number of pages5
JournalarXiv
Publication statusUnpublished - 10 Mar 2021

Keywords

  • eess.IV
  • cs.CV

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