The popularity of streaming videos with live, high-action content has led to an increased interest in High Frame Rate (HFR) videos. In this work we address the problem of frame rate dependent Video Quality Assessment (VQA) when the videos to be compared have different frame rate and compression factor. The current VQA models such as VMAF have superior correlation with perceptual judgments when videos to be compared have same frame rates and contain conventional distortions such as compression, scaling etc. However this framework requires additional pre-processing step when videos with different frame rates need to be compared, which can potentially limit its overall performance. Recently, Generalized Entropic Difference (GREED) VQA model was proposed to account for artifacts that arise due to changes in frame rate, and showed superior performance on the LIVE-YT-HFR database which contains frame rate dependent artifacts such as judder, strobing etc. In this paper we propose a simple extension, where the features from VMAF and GREED are fused in order to exploit the advantages of both models. We show through various experiments that the proposed fusion framework results in more efficient features for predicting frame rate dependent video quality. We also evaluate the fused feature set on standard non-HFR VQA databases and obtain superior performance than both GREED and VMAF, indicating the combined feature set captures complimentary perceptual quality information.
翻译:在这项工作中,当要比较的视频有不同的框架率和压缩系数时,我们处理框架速率取决于视频质量评估(VQA)的问题。 目前的VQA模型,如VMAF, 与视频比较时的光速率和包含压缩、缩放等常规扭曲等的感知性判断高度相关。 但是,当需要比较不同框架率的视频时,这一框架要求额外的处理前步骤,这有可能限制其总体性能。 最近,普遍通缩差异(GREED) VQA模型被提议对因框架率变化而产生的艺术品进行核算,并在LIVE-YT-HFR数据库中显示优异性性性性能,该数据库包含像judder、Strabing等根据性能判断得出的框架性能判断。 在本文中,我们建议一个简单的扩展,将VMAF和GREED的特征结合起来,以便利用两种模型的优势。我们通过各种实验显示,拟议的高端性能差异(GRE)质量框架在VDA标准性能模型中也显示高的性能性能性能预测。