In this letter, a method called Semantic Information Oriented No-Reference (SIONR) video quality assessment model is developed, which can effectively represent quality degradation of video by taking the variations of semantic information into consideration. Specially, temporal variations of the semantic features between adjacent frames are calculated to consider the inconsistency of the static semantic information. Moreover, low-level features are also applied as a supplementary to take distortions related to local details into consideration. Experimental results demonstrate that our proposed method obtains competitive performance compared with state-of-the-art methods in the two databases. Also, our model achieves good generalization capability. The code is available at: https://github.com/lorenzowu/SIONR .

中文翻译：

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面向语义信息的无参考视频质量评估

在这封信中，开发了一种称为面向语义信息的无参考（SIONR）视频质量评估模型的方法，该方法可以通过考虑语义信息的变化来有效表示视频的质量下降。特别地，计算相邻帧之间的语义特征的时间变化以考虑静态语义信息的不一致。此外，还使用低级功能作为补充，以考虑与局部细节有关的变形。实验结果表明，与两个数据库中的最新方法相比，我们提出的方法具有竞争优势。而且，我们的模型具有良好的泛化能力。该代码位于：https://github.com/lorenzowu/SIONR 。