The popularity of video on-demand streaming services increased tremendously over the last years. Most services use http-based adaptive video streaming methods. Today’s movies and TV shows are typically recorded in UHD-1/4K and streamed using settings attuned to the end-device and current network conditions. Video quality prediction models can be used to perform an extensive analysis of video codec settings to ensure high quality. Hence, we present a framework for the development of pixel-based video quality models. We instantiate four different model variants ( hyfr , hyfu , fume and nofu ) for short-term video quality estimation targeting various use cases. Our models range from a no-reference video quality model to a full-reference model including hybrid model extensions that incorporate client accessible meta-data. All models share a similar architecture and the same core features, depending on their mode of operation. Besides traditional mean opinion score prediction, we tackle quality estimation as a classification and multi-output regression problem. Our performance evaluation is based on the publicly available AVT-VQDB-UHD-1 dataset. We further evaluate the introduced center-cropping approach to speed up calculations. Our analysis shows that our hybrid full-reference model ( hyfr ) performs best, e.g. 0.92 PCC for MOS prediction, followed by the hybrid no-reference model ( hyfu ), full-reference model ( fume ) and no-reference model ( nofu ). We further show that our models outperform popular state-of-the-art models. The introduced features and machine-learning pipeline are publicly available for use by the community for further research and extension.

中文翻译：

---

UHD-1 / 4K的基于像素的视频质量模型的模块化框架和实例

过去几年中，视频点播流服务的受欢迎程度大大提高了。大多数服务使用基于http的自适应视频流方法。当今的电影和电视节目通常以UHD-1 / 4K录制，并使用根据终端设备和当前网络状况调整的设置进行流式传输。视频质量预测模型可用于对视频编解码器设置进行广泛的分析，以确保高质量。因此，我们提出了一个用于开发基于像素的视频质量模型的框架。我们实例化了四个不同的模型变体（ 海弗 ， 海夫 ， 烟 和 诺夫 ），以针对各种用例进行短期视频质量估算。我们的模型范围从无参考视频质量模型到全参考模型，包括结合了客户端可访问元数据的混合模型扩展。所有模型都根据其操作模式共享相似的体系结构和相同的核心功能。除了传统的平均意见得分预测之外，我们还将质量估计作为一种分类和多输出回归问题进行处理。我们的性能评估基于公开可用的AVT-VQDB-UHD-1数据集。我们进一步评估了引入的中心裁剪方法以加快计算速度。我们的分析表明，我们的混合全参考模型（ 海弗 ）表现最好，例如0.92 PCC用于MOS预测，其次是混合无参考模型（ 海夫 ），完整参考模型（ 烟 ）和无参考模型（ 诺夫 ）。我们进一步证明，我们的模型优于流行的最新模型。引入的功能和机器学习管道已公开提供给社区，以供进一步研究和扩展。