通过新的特征集成和模型组合增强VMAF
Enhancing VMAF through New Feature Integration and Model Combination
摘要：
VMAF是一种基于机器学习的视频质量评估方法，最初是为流媒体应用设计的，它通过SVM回归将多个质量指标和视频特征结合起来。它提供了更高的相关性与主观意见相比，许多传统的质量评估方法。在本文中，我们提出了通过集成新的视频功能和替代质量指标（从不同的池中选择）以及多个模型组合来增强VMAF。所提出的组合方法使得能够在具有不同内容和失真特性的多个数据库上进行训练。我们的增强VMAF方法已经在八个高清视频数据库上进行了评估，并且始终优于原始VMAF模型（0.6.1）和其他基准质量指标，表现出与主观地面实况数据更高的相关性。
英文摘要：
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.
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